The Nigerian capital market plays a critical role in mobilizing long-term funds for economic growth; however, it is characterized by information asymmetry, market inefficiencies, and high volatility that limit accurate price discovery and optimal investment decision-making. Recent advances in predictive artificial intelligence (AI) and machine learning (ML) present new opportunities to enhance business analytics and improve market forecasting, particularly in emerging markets such as Nigeria. This study empirically investigates the effectiveness of AI- and machine learning–driven business analytics in forecasting stock returns and improving market efficiency in the Nigerian capital market. Using historical equity price data from selected firms listed on the Nigerian Exchange (NGX), alongside relevant macroeconomic indicators, the study develops and compares multiple predictive models, including traditional econometric approaches and advanced machine learning algorithms such as Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) neural networks. Model performance is evaluated using standard predictive accuracy metrics and economic performance indicators, including return predictability and risk-adjusted investment outcomes. Feature importance and explainability techniques are employed to identify key drivers of market movements and enhance model interpretability for decision-makers. The findings are expected to demonstrate whether machine learning–based predictive analytics significantly outperform conventional models in capturing nonlinear patterns and temporal dependencies inherent in Nigerian equity market data. By quantifying the economic value of AI-driven forecasts, the study contributes empirical evidence on how predictive analytics can translate data insights into improved investment strategies and portfolio performance. The research further discusses implications for investors, financial analysts, and market regulators, highlighting the potential of AI-enabled business analytics to support more efficient capital allocation, enhance transparency, and strengthen market stability in Nigeria. Overall, the study provides practical and policy-relevant insights into the transformative role of predictive AI in emerging capital markets.