When it comes to early diagnosis in particular, predictive analytics—made possible in large part by machine learning—has the potential to radically alter the healthcare system. Predictive analytics has many uses in healthcare, but this review paper mainly discusses how it might help with early diagnosis. Using the possible advantages of using machine learning algorithms for predictive modeling, we address the significance of early diagnosis in enhancing patient outcomes and decreasing healthcare expenses. Early diagnosis across many medical problems is investigated using a variety of machine learning techniques, such as supervised, unsupervised, and deep learning approaches. We also look at the difficulties of integrating predictive analytics into preexisting clinical workflows, issues with data protection, and the interpretability of models. We conclude by outlining potential avenues for further study and development in this dynamic area, with an emphasis on the importance of data scientists, healthcare providers, and legislators working together to fully utilize predictive analytics for healthcare early diagnosis.