Marathi lemmatization, the process of identifying a word’s base form or lemma, is crucial in natural language processing applications such as text mining, machine translation, and information retrieval. This research provides a comprehensive analysis of using machine learning approaches for Marathi lemmatization, focusing on comparing different classification algorithms. We employ a character-level Count Vectorizer to convert wordform-lemma pairs into numerical vectors, subsequently training classifiers like Support Vector Machine (SVM), Random Forest, Decision Tree, and Logistic Regression. Experiments are conducted using a dataset of Marathi wordform-lemma pairs, with each algorithm’s performance evaluated through accuracy metrics. A key finding is that while SVM achieves the highest overall accuracy, Random Forest performs better with random words, suggesting greater resilience and efficiency when processing unknown inputs. Our findings offer valuable insights into selecting appropriate machine learning algorithms for Marathi lemmatization, considering factors like accuracy, robustness, and practicality. Additionally, this work lays the foundation for future research in Marathi language processing, enhancing our understanding of this field.