Machine learning, a class of artificial intelligence (AI), is widely used for big data analytics. It is now vastly used for accomplishing the Robot Navigation task by simply applying different algorithms. These algorithms convert user-generated commands into machine-understandable language. This is done by a wall-following control that is a robot’s movements in arbitrary directions while maintaining a specific distance from a particular wall. This paper illustrates two leading research contributions. Firstly, it discusses the significance of ML models in Robot navigations. Secondly, this paper comprises a detailed study and comparative analysis on the execution of different ML and Deep Learning algorithms using all three robot navigation formats (short, full, and simpler). In this paper, the evaluations and assessments of all the models are done by Monte-Carlo cross-validation.