Jordanian Journal of Informatics and Computing

Login

The Role of Artificial Intelligence in Bodybuilding: A Systematic Review of Applications, Challenges, and Future Prospects

by 

Mahmood A. Al-Shareeda ;

Ahmed Abdulazeez Obaid ;

Amjad Abdul Hamid Almaji

PDF logoPDF

Published: 2025/02/20

Abstract

Artificial Intelligence (AI) is making more and more impact on bodybuilding and helps providing data driven insights for improved training, nutrition, performance analytics, injury prevention and supplementation. This article systematically reviews the impact of AI on five key aspects of bodybuilding. For example, the adaptive workout plans and real-time training feedback in AI-Based Training Optimization improve progressive overload and movement accuracy. Second, it is AI-Driven Nutrition & Diet Planning that will refine macronutrient tracking, each meal customization, and genetics-based diet optimization. Thirdly, AI in Performance Monitoring & Biomechanics uses wearables and computer vision to correct exercise form and analyze body composition. The fourth challenge AI solves is in Injury Prevention & Recovery - using world-class predictive models for muscle strain detection, personalized rehabilitation, and optimized rest protocols. Finally, it forms a part of the AI in Supplementation & Pharmacology encompassing supplement initiatives, hormonal regulation, performance-enhancing drugs, and its detection. However, existing AI systems encounter difficulties, including limited adaptability to individual physiology, inherent dataset biases, privacy issues, and the absence of regulatory frameworks for AI-assisted supplementation and doping detection. Looking ahead, we will have smart gym gear, hybrid AI and human coaches, nutrigenomics supported by AI, and regenerative medicine methodologies. This review highlights the excesses of bodybuilding and the potentiality of AI in moderating bodybuilding under the normative edges of ethics, regulations, and human expertise.

Keywords

Artificial IntelligenceBodybuildingMachine LearningAI in FitnessPerformance MonitoringAI in NutritionInjury PreventionAI in Supplementation

The Role of Artificial Intelligence in Bodybuilding: A Systematic Review of Applications, Challenges, and Future Prospects is licensed under CC BY 4.0CCBY

References

  1. R. Perez-Lopez, N. Ghaffari Laleh, F. Mahmood, J. N. Kather, A guide to artificial intelligence for cancer researchers, Nature Reviews Cancer 24 (6) (2024) 427–441.
  2. V. Bolón-Canedo, L. Morán-Fernández, B. Cancela, A. Alonso- Betanzos, A review of green artificial intelligence: Towards a more sus- tainable future, Neurocomputing (2024) 128096.
  3. C. Xiang, Comparative the scoring system of bodybuilding competition based on ict technology, in: 2021 3rd International Conference on Arti- ficial Intelligence and Advanced Manufacture, 2021, pp. 1097–1101.
  4. X. Cheng, et al., Research on smart fitness and bodybuilding based on wireless network sensor action monitoring device, International Journal of New Developments in Engineering and Society 6 (1) (2022).
  5. D. Liokaftos, Natural bodybuilding: An account of its emergence and development as competition sport, International Review for the Sociol- ogy of Sport 54 (6) (2019) 753–770.
  6. A. Rukstela, K. Lafontant, E. Helms, G. Escalante, K. Phillips, B. I. Campbell, Bodybuilding coaching strategies meet evidence-based recom- mendations: A qualitative approach, Journal of Functional Morphology and Kinesiology 8 (2) (2023) 84.
  7. E. F. Willmott, S. N. Thrower, A. Petróczi, Grow through what you go through: a multiple-case study of competitive bodybuilders’ experiences of learning to manage the demands of their engagement in the sport, Qualitative Research in Sport, Exercise and Health (2025) 1–17.
  8. P. A. Keane, E. J. Topol, Ai-facilitated health care requires education of clinicians, The Lancet 397 (10281) (2021) 1254.
  9. H. Schopmans, J. Cupać, Engines of patriarchy: Ethical artificial intelligence in times of illiberal backlash politics, Ethics & International Affairs 35 (3) (2021) 329–342.
  10. Rathore, A. S., Nikita, S., Thakur, G., & Mishra, S. (2023). Artificial intelligence and machine learning applications in biopharmaceutical manufacturing. Trends in Biotechnology, 41(4), 497-510.
  11. G. Chhabra, S. Kumar, S. Gupta, P. Nagpal, Technology for nutrition, fitness, and sports, in: Artificial Intelligence to Analyze Psychophysical and Human Lifestyle, Springer, 2023, pp. 105–112.
  12. E. Mikołajewska, D. Mikołajewski, T. Mikołajczyk, T. Paczkowski, A breakthrough in producing personalized solutions for rehabilitation and physiotherapy thanks to the introduction of AI to additive manufacturing, Applied Sciences 15 (4) (2025) 2219.
  13. L. Wan, Design and adjustment of optimizing athletes’ training pro- grams using machine learning algorithms, J. Electrical Systems 20 (6s) (2024) 2014–2024.
  14. X. Han, X. Zhou, B. Tan, L. Jiao, R. Zhang, Ai-based next-generation sensors for enhanced rehabilitation monitoring and analysis, Measurement 223 (2023) 113758.
  15. H. Kassem, A. A. Beevi, S. Basheer, G. Lutfi, L. Cheikh Ismail, D. Pa- pandreou, Investigation and assessment of ai’s role in nutrition—an up- dated narrative review of the evidence, Nutrients 17 (1) (2025) 190.
  16. Y. Liang, R. Xiao, F. Huang, Q. Lin, J. Guo, W. Zeng, J. Dong, Ai nutritionist: Intelligent software as the next generation pioneer of precision nutrition, Computers in Biology and Medicine (2024) 108711.
  17. G. Prasad, M. Padhiary, A. Hoque, K. Kumar, Ai-driven personalized nutrition apps and platforms for enhanced diet and wellness, in: Food in the Metaverse and Web 3.0 Era: Intersecting Food, Technology, and Culture, IGI Global Scientific Publishing, 2025, pp. 125–158.
  18. K. C. Onyelowe, A. M. Ebid, P. Awoyera, V. Kamchoom, E. Rosero, M. Albuja, C. Mancheno, Prediction and validation of mechanical properties of self-compacting geopolymer concrete using combined machine learning methods a comparative and suitability assessment of the best analysis, Scientific Reports 15 (1) (2025) 1–53.
  19. A. Jagani, D. Katudia, M. Darji, V. Hirlekar, Cogniposeai: A futuristic AI-enhanced personal trainer, in: 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), IEEE, 2023, pp. 188–193.
  20. A. Tannoury, E. M. Choueiri, R. Darazi, Human pose estimation using depth-wise separable convolutional neural networks, World Safety Journal (WSJ) 31 (3) 39.
  21. F. Chaabane, Multi-source data fusion to enhance wireless communication beyond 5g for smart city transformation, in: Internet of Vehicles and Computer Vision Solutions for Smart City Transformations, Springer, 2025, pp. 451–473.
  22. E. Garosi, F. Sheikh, M. Goodarzi, Ergonomic interventions in risk re- duction (2025).
  23. R. R. Shigapova, Y. O. Mukhamedshina, Electrophysiology methods for assessing of neurodegenerative and post-traumatic processes as applied to translational research, Life 14 (6) (2024) 737.
  24. Q. Luo, N. Huang, D. L. Bartles, T. W. Simpson, A. M. Beese, Harnessing of in situ processing data to predict mechanical properties of laser powder bed fusion alsi10mg, Journal of Intelligent Manufacturing (2025) 1–23.
  25. A. Sharma, B. Grant, H. Islam, A. Kapoor, A. Pradeep, C. N. Jayasena, Common symptoms associated with usage and cessation of anabolic androgenic steroids in men, Best Practice & Research Clinical Endocrinology & Metabolism 36 (5) (2022) 101691.
  26. N. Leciejewska, K. Jędrejko, V. M. Gómez-Renaud, J. Manríquez- Núñez, B. Muszyńska, A. Pokrywka, Selective androgen receptor modulator use and related adverse events including drug-induced liver injury: analysis of suspected cases, European Journal of Clinical Pharmacology 80 (2) (2024) 185–202.