Tarbiat Modares University International Journal of Musculoskeletal Pain Prevention 2476-5279 11 4 2026 09 01 An Analytic Review of Current Approaches to Anterior Cruciate Ligament Reconstruction: Integrating Surgical Techniques, Graft Selection, and Emerging Role of Artificial Intelligence 1476 1480 29016 10.48311/ijmpp.2026.119796.82921 EN Ebrahim Piri University of Mohaghegh Ardabili 0000-0001-9188-9746 Mahsa Habibi Department of Sports Biomechanics, Faculty of Educational Sciences and Psychology, University of Mohaghegh Ardabili, Ardabil, Iran. 0009-0002-2336-3958 Mahdiyeh Foroughi Department of Sports Biomechanics, Faculty of Educational Sciences and Psychology, University of Mohaghegh Ardabili, Ardabil, Iran. 0009-0002-9163-6268 Journal Article 2026 05 13 Aims: The purpose of this analytic review is to investigate the current surgical strategies for Anterior Cruciate Ligament (ACL) reconstruction, critically examine graft selection criteria, highlight emerging technological supports such as Artificial Intelligence (AI), and provide integrative insights for improving post-surgical outcomes.Method and Materials: Major developments in the biomechanical understanding of ACL injury and associated reconstructive procedures were extracted from contemporary literature. Analytical comparison was performed across surgical methods, graft choices, and computational innovations influencing clinical decision-making. Findings: Historical and contemporary findings indicate that surgical evolution, particularly toward anatomic reconstruction and individualized graft selection, has significantly enhanced biomechanical stability and recovery. Artificial intelligence shows promise in optimizing diagnosis, patient stratification, and rehabilitation protocols through data-driven models. Conclusion: Anterior Cruciate Ligament reconstruction strategies continue to evolve towards precision-based care integrating biomechanics, surgical innovation, and computational assistance. Future research should focus on the synergy between surgical experience and algorithmic prediction to maximize personalized treatment outcomes. https://ijmpp.modares.ac.ir/article_29016_85c8395916ffc2198dd670da1b20d108.pdf