Metaheuristic Optimization Review MOR Pending 10.54216/MOR https://www.americaspg.com/journals/show/3409 2024 2024 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia Doaa Doaa School of Computer Science, University of Petroleum and Energy Studies, Dehradun, 248001, India Sunil Kumar Future stock price prediction is one of the most important and complex tasks in the lecture on finance, mainly due to the characteristics of the financial world. Machine learning techniques have greatly improved this area: problems with frequent data and nonlinear processes, which cannot be solved using conventional models, have been solved. In this paper, the author looks at how the methodology of data preprocessing and two modeling techniques, namely, the high-frequency data model and the sentiment analysis model, have helped improve the efficiency of stock price forecasts. Among the proposed techniques, Temporal Convolutional Networks (TCN), Attention Mechanisms, and Transformer-based architectures are mentioned due to their capability to distill complex market dynamics. However, issues like data quality and fluctuations in the market remain sticky even as we see the speed of innovation picking up, and thus, the importance of model robustness and interpretability. Drawing on recent advances and mapping out the directions for future studies, this paper reveals how machine learning may revolutionize stock market prediction and investment decision-making in a continuously transforming financial environment. 2024 2024 53 63 10.54216/MOR.020105 https://www.americaspg.com/articleinfo/41/show/3409