Myocardial infarction (MI) is still one of the most common causes of death and illness globally. Complications after MI have a big effect on how well patients do. Finding risk factors for these issues that can and can’t be changed is very important for designing targeted prevention and care plans. This study aimed to identify significant associations between various clinical and demographic exposures and the occurrence of common post-MI complications, using a publicly available dataset. We got the Myocardial Infarction Complications dataset (ID = 579) from the UCI Machine Learning Repository. Data preparation meant dealing with categorical variables that weren’t balanced by dropping features where one class made up more than 80% of the observations and dealing with missing values by removing features that were more than 30% missing. We normalized the continuous predictor variables. We analyzed associations between 28 clinical and demographic exposures and 11 common post-MI complications using multivariate logistic regression. After controlling for key confounders, we identified 18 significant exposure-outcome links, including strong associations between tachycardia at admission and pulmonary edema and between specific MI locations and mechanical complications. For example, sinus tachycardia at admission (ritm_ecg_p_07) was associated with pulmonary edema (OTEK_LANC) (OR 2.25, 95% CI [1.53, 3.31]), and lateral MI (lat_im) with myocardial rupture (RAZRIV) (OR 1.97, 95% CI [1.50, 2.59]). This study found that certain clinical variables were strongly linked to a higher incidence of certain sequelae after a heart attack. These findings warrant further investigation and may contribute to improved risk stratification and management in post-MI care.