Machine Learning (ML) has emerged as a powerful tool for solving complex scientific problems, driving advancements across various fields such as biology, physics, chemistry, and environmental science. This review paper highlights the intersection of ML and scientific research, focusing on key algorithms, popular applications, and unique challenges faced by scientists. With a growing number of researchers leveraging ML to analyze large datasets, make predictions, and uncover hidden patterns, this paper provides an overview of machine learning techniques, their applications in scientific domains, and the challenges scientists face in integrating ML into their workflows