The adoption of various traffic prediction technologies has led to recent enhancements in the efficacy of the transportation system. The Intelligent Transport System (ITS) organization is dedicated to the regulation of traffic and the development of uses for future transportation systems. The forecast of traffic flow holds significant utility in the management of traffic flow, optimization of traffic signal systems, and estimation of travel durations. In this program, user input is collected and utilized to employ various machine learning algorithms for the purpose of predicting traffic flow. This study aims to conduct a comparative analysis of different traffic analysis methods, including Random Forest Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Gated Recurrent Units (GRU). The objective is to determine the most effective model by applying these methods to a comprehensive dataset of traffic. The highest performing model has the potential to be utilized for future predictions of traffic flow.