Artificial intelligence (AI) has transformed elite basketball performance analysis by enabling the collection, integration, and interpretation of complex performance data. Although previous reviews have summarized AI applications in sport, relatively few have synthesized how these technologies support performance analysis and evidence-informed coaching decision-making in elite basketball. Evidence remains fragmented across prediction, tactical analysis, workload monitoring, athlete development, and coaching support. This structured narrative review synthesizes AI applications in elite basketball performance analysis and examines their implications for coaching practice, player monitoring, tactical interpretation, and evidence-informed decision-making. Particular attention is given to methodological challenges, practical implementation, and ethical considerations influencing AI adoption in elite basketball. Across the literature, AI applications have evolved beyond performance prediction toward decision-support systems for tactical analysis, player tracking, wearable monitoring, and individualized athlete management. Computer vision, tracking technologies, and wearable sensing provide comprehensive information on spatiotemporal behaviour, movement coordination, tactical organization, and athlete workload. However, implementation lags behind technological development because of limited external validation and ecological validity, fragmented datasets, limited interpretability, and difficulty translating AI-generated outputs into meaningful coaching decisions. Ethical concerns regarding privacy, biometric data governance, fairness, and algorithmic transparency remain barriers to responsible implementation. Rather than replacing coaching expertise, current evidence supports AI as a complementary decision-support tool that enhances human judgement in elite basketball. AI has considerable potential to strengthen performance analysis and evidence-informed coaching in elite basketball when supported by valid, interpretable, and ethically governed decision-support systems. Future research should prioritize longitudinal validation, explainable AI, integrated data ecosystems, coach-centred system design, and responsible governance to facilitate translation of AI innovations into high-performance basketball practice.