Molecules with potent anti-leishmanial activity play a crucial role in identifying treatments for leishmaniasis and aiding in the design of novel drugs to combat the disease, ultimately protecting individuals and populations. Various methods have been employed to represent molecular structures and predict effective anti-leishmanial molecules. However, each method faces challenges and limitations that must be addressed to optimize the drug discovery and design process. Recently, machine learning approaches have gained significant importance in overcoming the limitations of traditional methods across various fields. Therefore, there is an urgent need to build a computational pipeline using advanced machine learning and deep learning methods that help to predict anti-leishmanial activity of drug candidates. The proposed pipeline in this paper involves data collection, feature extraction, feature selection and prediction techniques. This review presents a comprehensive computational pipeline for anti-leishmanial drug discovery, highlighting its strengths, limitations, challenges, and future directions to improve treatment for this neglected tropical disease.