Traffic congestion is a critical issue plaguing urban areas, with substantial economic, environmental, and social implications. Machine learning (ML) techniques have emerged as transssformative tools in addressing these challenges by leveraging vast datasets and complex algorithms to predict and traffic congestion. This paper comprehensively reviews existing ML and deep learning methods for traffic congestion prediction, highlighting their strengths, limitations, and real-world applications. The paper also identifies key challenges in applying ML techniques to traffic forecasting and proposes future advancements to enhance prediction accuracy and reliability. Comparative analyses of popular algorithms such as CNN, AdaBoost, and ANN are presented, providing insights into their relative performance. Finally, avenues for integrating hybrid models and real-time data streams are explored to promote innovation in transportation management systems. In this paper, the proposed work has presented various existing traffic congestion with merits and demerits.