Stock market prediction remains a challenging problem due to market complexity and evolving dynamics. This paper reviews recent modeling approaches, analyzing their strengths and limitations. We examine information-theoretic methods for quantifying uncertainty, NLP techniques for sentiment extraction, and deep learning architectures for temporal pattern recognition, along with other emerging methodologies. Key findings reveal that while entropy-based models effectively capture nonlinear dependencies, they face computational constraints. Sentiment analysis adds behavioral insights but struggles with noise and context dependence. Deep learning models, particularly LSTMs, excel at pattern recognition but require complementary features for robust performance. Hybrid approaches combining these techniques show promise but need improvements in interpretability and adaptability. The review identifies critical gaps in current research, including the need for standardized evaluation protocols and better handling of non-stationary market conditions. Future work should focus on developing adaptive frameworks that integrate multiple methodologies while addressing computational and practical implementation challenges. This analysis provides valuable insights for advancing stock prediction models, balancing theoretical rigor with real-world applicability.