The task of disambiguating word senses, often referred to as Word Sense Disambiguation (WSD), is a substantial difficulty in the realm of natural language processing. Marathi is widely acknowledged as a language that has a relatively restricted range of resources. Consequently, there has been a paucity of academic research undertaken on the Marathi language. There has been little research conducted on supervised learning for Marathi Word Sense Disambiguation (WSD) mostly owing to the scarcity of sense-annotated corpora. This work aims to construct a sense-annotated corpus for the Marathi language and further use supervised learning classifiers, such as Naïve Bayes, Support Vector Machine, Random Forest, and Logistic Regression, to disambiguate polysemous words in Marathi. The performance of these classifiers is evaluated.