The integration of Artificial Intelligence (AI) into women’s Kabaddi performance has emerged as a transformative development in modern sports science. This study examines the multifaceted role of AI in enhancing tactical efficiency, training optimization, injury prevention, and talent identification among women Kabaddi players. Using secondary data sourced from published research articles, conference proceedings, news reports, and analytical sports platforms, the study highlights how AI-driven systems—such as computer vision, machine learning models, wearable sensors, and predictive analytics—contribute to individualized, data-centric performance enhancement. The findings reveal that AI provides granular spatiotemporal and biomechanical insights, supports gender-specific training interventions, and improves decision-making through advanced tactical analysis. Additionally, AI enhances injury prediction by analyzing multivariate time-series data and integrating physiological variables unique to female athletes, such as hormonal cyclicity. The study concludes that AI-driven methodologies shift women’s Kabaddi from traditional heuristic coaching to a validated, evidence-based, and technologically adaptive framework, enabling sustainable performance improvement and athlete safety.