<p>With the rapid evolution of technologies like IoT (Internet of Things) and ML (Machine Learning), applications are becoming much smarter, thus giving more opportunity for the exploitation of various sectors. With such connectivity of devices giving rise to abundant amount of data thus boosting the deployment of Machine learning. Though Machine learning has found immense applicability in various sector, transportation is one such sectors that has attracted many researchers for transforming the paradigm of transportation into intelligence and enhancement. During the review, we considered the various layers of Machine learning in Transportation and reviewed them one by one. The application area of ML in transportation is reviewed thoroughly, discussing the recent advancements carried out in areas such as route optimization, logistics, accident detection, and many others. The purpose of the review is to present a self-contained critical review discussing every aspect of the deployment of the approach in Transportation. From the review, it is found that though ML is a strong aspect for the future revolution of transportation systems, there are some challenges, such as privacy concerns, that cannot be ignored and need good research to overcome these challenges.</p>