Myocardial infarction, known to be heart attack, is still a major global health concern. Recognizing and categorizing MI complications in a timely manner is crucial for effective patient care and better outcomes. In this study, we present an innovative approach that harness power of machine learning to classify complications associated with myocardial infarction using a wide range of clinical data. Our study draws from an extensive dataset that encompasses patient records, imaging data, and lab results. An integral component of our approach involves employing feature selection techniques to pinpoint the most relevant predictors from the vast pool of clinical information. This not only makes the model more understandable but also enhances its predictive accuracy by curbing overfitting and computational complexities. We make use of advanced ML algorithms such as random forests, support vector machines, and deep neural networks, to delve into the intricate relationships hidden within the data.