Myocardial Infarction remains a high-mortality cardiovascular disease. While ST-Elevation Myocardial Infarction (STEMI) is the traditional intervention standard, many Occlusion Myocardial Infarction (OMI) cases are misclassified as Non-ST Elevation Myocardial Infarction (NSTEMI), leading to delayed treatment and poor outcomes. This study collected clinical and 12-lead ECG data from AMI patients over five years, categorized into OMI and Non Occlusion Myocardial Infarction (NOMI) groups based on angiography and cardiac enzymes. We developed a ResNet-1D deep learning model to identify OMI from Electrocardiograms (ECG) signal patterns. The model achieved an OMI recall of 0.75 and specificity to 0.53. Our findings suggest that while ST-elevation remains a primary OMI indicator, machine learning can effectively assist clinicians in detecting hidden OMI cases within the NSTEMI population, providing critical diagnostic support alongside clinical analysis.