This chapter focuses on the development and evaluation of prediction models for heart failure using machine learning techniques. By analyzing patient characteristics and applying algorithms such as random forest, gradient boosting, and others, highly accurate predictions were achieved. The findings demonstrate that machine learning models can provide reliable tools for the early diagnosis and prevention of heart failure. The final application, developed in Django, proved effective with real-world data, offering significant support to healthcare professionals. Despite the study’s limitations, the results highlight the potential of machine learning in medical prediction and suggest directions for future research.