The term "Intelligent Transportation Systems" (ITS) refers to the incorporation of contemporary technology into transportation infrastructure and vehicles with the goal of enhancing overall mobility, safety, and efficiency. By anticipating traffic patterns, congestion, and trip times, traffic forecasting is an essential component of intelligent transportation systems (ITS). Forecasts that are accurate allow for more effective control of traffic, the optimization of routes, and an overall improvement in the operation of the transportation system. In the beginning, techniques of forecasting depended on straightforward statistical models and the examination of historical data. The development of computer technology led to the introduction of strategies and algorithms that were more complicated and simulation methods. The field of traffic forecasting has seen a sea change in recent years as a result of the introduction of machine learning algorithms, which are driven by large datasets and processing capabilities. The challenge of traffic forecasting is to make predictions about the future circumstances of traffic. These predictions are based on both historical and current data. This involves estimating the amount of traffic, the level of congestion, and the amount of time it will take to travel on particular routes. The complexity of urban transportation networks, which are impacted by a variety of factors including weather, accidents, events, and urban expansion, is a challenge that must be overcome