The integration of computational intelligence and data-driven analytics has revolutionized sports science, enabling precise forecasting of game outcomes, player performance, and team strategy. This chapter presents a comprehensive exploration of sport outcome prediction through machine learning (ML) and artificial intelligence (AI) techniques. Beginning with an overview of predictive analytics and its multifaceted applications in sports, the discussion extends to essential data collection and preprocessing strategies that underpin model accuracy. The chapter highlights the critical role of feature selection and engineering in extracting meaningful patterns from diverse data sources such as player statistics, tracking data, and contextual variables. It then surveys a broad spectrum of ML algorithms—including supervised, unsupervised, semi-supervised, reinforcement, and deep learning methods—commonly employed in outcome forecasting. Comparative insights from existing review studies illustrate the evolution and diversity of research in this field. Furthermore, the chapter maps computational prediction across various sports domains, from soccer and basketball to less explored areas such as gymnastics and cycling. Concluding sections identify key challenges—such as data heterogeneity, ethical considerations, and model interpretability—and propose future research directions aimed at enhancing predictive accuracy, transparency, and practical impact. Overall, this work underscores how computational approaches can transform sports analytics into a strategic tool for performance optimization, talent management, and data-informed decision-making.
