Traffic congestion is a major problem faced by cities all over the world, leading to increased travel time, fuel consumption and environmental pollution. Accurate traffic forecasting can play an important role in improving traffic management and reducing congestion. This article presents a non-plagiarized summary on the topic of traffic flow prediction using machine learning. The goal of this study was to develop a machine learning-based approach to traffic flow prediction, leveraging historical traffic data and other relevant factors. Various machine learning algorithms, including regression modeling, time series analysis, and deep learning techniques, are explored and compared to determine the most effective method. To do this, a comprehensive dataset containing historical data on traffic volumes, weather conditions, road infrastructure and other relevant features is collected. Data preprocessing techniques are applied to clean and convert the data set into a format suitable for analysis. Feature selection methods are used to identify the factors that have the most influence on traffic flow. Methods: Several machine learning models are trained and evaluated using the collected data set. The models are tested on unpublished data to assess their accuracy and predictive certainty. Performance measurements such as mean absolute error (MAE), root mean square error (RMSE) and R-squared are used to evaluate and compare the performance of the model. Results: The results demonstrate that machine learning techniques offer a promising solution for traffic flow prediction. The study identifies the most accurate and effective model for predicting traffic based on specific data sets and review metrics. In addition, the study provides insight into the important features and factors that affect traffic, helping transportation regulators and planners make informed decisions about management. Transport and improve infrastructure. Conclusions: Overall, this study contributes to the field of traffic forecasting by highlighting the potential of machine learning techniques in accurately predicting traffic patterns. The results highlight the importance of using historical data and related features to improve forecast accuracy. The models developed and the insights gained from this research can be used to develop intelligent traffic management systems that optimize the timing of traffic signals and aid in planning efficient transportation to reduce congestion and improve the overall urban mobility experience.