Intelligent Transportation Systems (ITS) provides the state-of-the-art real time integration of vehicles and intelligent systems. Collectively, the prospective of the technologies have capability to communicate between system users, roads, and infrastructure. This study presents a comprehensive examination of many applications and implications of AI and ML in the development of an ITS. The primary objective of this is to effectively mitigate the traffic congestion and enhance road safety measures to prevent accidents. Subsequently, we examined different machine learning methodologies employed in the identification of road traffic based on vehicles and their junctions with the purpose of evading impediments, as well as forecasting real-time traffic patterns to attain intelligent and effective transportation systems. The exponential growth of the population inside the country has resulted in a corresponding rise in the utilization of vehicles and various modes of transportation, thereby it needs a contributing to the exacerbation of traffic congestion and the occurrence of road accidents. Therefore, there exists a need for intelligent transportation systems that possess the capability to offer the dependable transportation services while simultaneously upholding environmental standards to overcome the traffic congestions. Designing accurate models for predicting traffic density is a crucial task in the field of transportation systems. This study compares the ML models which are derived using a variety of machine-learning approaches. Supervised machine learning algorithms, including Naive Bayes, Markov models, KNN, linear regression, and SVM, and KNN are employed. The conclusion result suggests that the Markov model achieves the highest level of accuracy, of 98%. Implementation of ITS with Markov Model provides the best performance in resilient environment.