Research papers in stock market prediction using machine learning investigate computational models to forecast stock prices, returns, or market trends by analyzing historical financial data, technical indicators, macroeconomic signals, and even news or social media sentiment. Traditional machine learning approaches include support vector machines (SVM), random forests, decision trees, logistic regression, and ensemble methods, which aim to capture non-linear relationships in stock market data. More advanced studies employ deep learning techniques such as recurrent neural networks (RNN), long short-term memory (LSTM), gated recurrent units (GRU), convolutional neural networks (CNN), and transformer-based architectures to model sequential dependencies and extract complex patterns from time-series and textual data. Hybrid approaches that combine technical analysis with natural language processing (NLP) for sentiment analysis of financial news and tweets are gaining significant attention. Research also explores reinforcement learning for portfolio optimization and adaptive trading strategies. Key challenges highlighted include data volatility, market noise, overfitting, and the difficulty of generalizing predictive models across different markets. Recent works emphasize explainable AI (XAI) for interpretability, as well as federated and transfer learning for cross-market adaptability. These studies aim to enhance investment decision-making, risk management, and algorithmic trading efficiency.