Rainfall is a pivotal rainfall parameter in the environment of India. Vaticination of downfall can effectively prop the decision-making process for husbandry and natural disaster operation of the country. Still the chaotic nature of downfall due to climate change has made the task of downfall vaticination challenging through traditional statistical models. Rainfall prediction is a crucial task with farreaching implications for agriculture, water resource management, and disaster preparedness. This paper investigates the performance of two popular machine learning algorithms, Random Forest and Linear Regression, for rainfall prediction. We compare their strengths and weaknesses based on their underlying principles, model complexity, and performance metrics, utilizing a publicly available rainfall dataset. The results demonstrate that Random Forest generally outperforms Linear Regression in capturing the non-linear relationships present in rainfall data, leading to more accurate predictions. However, we also discuss the advantages of Linear Regression in terms of interpretability and computational efficiency, highlighting the importance of considering specific application requirements when choosing an appropriate model.