Stock market prediction is a complex computational problem because financial prices are influenced by nonlinear patterns, historical dependencies, market trends, and rapidly changing information. Conventional statistical approaches often face difficulty in representing these interacting characteristics, motivating the adoption of machine learning, deep learning, sentiment analysis, and reinforcement learning techniques. This research and review paper examines intelligent approaches for stock market prediction and decision-making through a structured synthesis of the provided literature. The study comparatively analyzes regression-based models, support vector machines, machine learning algorithms, recurrent architectures, sentiment-enhanced prediction, and reinforcement learning approaches. Particular attention is given to the methodological implications of feature selection, temporal dependencies, model comparison, and the integration of heterogeneous information. The reviewed studies indicate that no single predictive model is universally optimal; rather, model effectiveness depends on the characteristics of the dataset, prediction horizon, selected features, and evaluation methodology. The analysis further proposes an intelligent prediction and decision-making framework integrating market data, sentiment information, advanced predictive models, confidence estimation, and decision rules. The resulting framework provides a conceptual basis for transforming price prediction into an actionable decision-support process while recognizing the limitations associated with market uncertainty, data quality, model instability, and generalization.