Professional sport clubs increasingly use artificial intelligence (AI) to forecast demand, personalize offers, automate service, optimize prices, and support retention across ticketing, merchandise, memberships, subscriptions, and digital products. Evidence on these applications remains fragmented across sport management, information systems, marketing, and electronic commerce. This integrative, theory-building narrative review synthesizes 80 analytical sources and uses one additional reporting-quality instrument to examine how organizational capabilities, AI-enabled practices, fan appraisals, outcomes, boundary conditions, and governance interact in club-controlled commerce ecosystems. The review develops a task-based taxonomy—sense and predict, personalize, interact and serve, price and convert, and retain and learn—and proposes a multilevel framework in which governed data, cross-functional orchestration, experimentation, ecosystem integration, and responsible-AI governance enable deployment. Fan-facing effects are proposed to operate through relevance, convenience, enjoyment, authenticity, interaction-specific trust, perceived control, and fairness, while some operational effects may arise more directly. Fan identification is proposed to amplify both positive responses to identity-congruent relevance and adverse responses to perceived opportunism. Nine proposition sets specify relationships that require direct club-level testing. The review concludes with a staged implementation sequence, a balanced measurement dashboard, and priorities for causal, longitudinal, comparative, and governance research.