Abstract Background The cumbersome, standard 12-lead electrocardiogram (EKG) challenges an efficient detection of ST-Elevation Myocardial Infarction (STEMI) in pre-hospital (ambulances) and hospital (portable devices) settings. We believe that our machine-learning algorithm embedded into a single lead EKG will be successful in acute care settings. Purpose To incorporate Artificial Intelligence-guided, single lead EKG interpretation, to facilitate easy and accurate STEMI detection in urgent situations. Methods This is an observational, retrospective, case-control study. A subset sample was generated from the International Telemedical Systems (ITMS) database that contains cardiologist annotated EKG records. Subset: A total of 2,542 exclusively confirmed STEMI diagnosis EKG records from enrolled healthcare centers in Mexico, Colombia, and Brazil; including specific ischemic heart wall (anterior, inferior, and lateral). Following discharge of treated patients, confirmation of STEMI diagnosis was obtained as feedback from healthcare centers. Records were anonymized EKG that excluded all medical information. Sample: A Standard 12 lead, 10-seconds length, 500Hz sampling frequency EKG was fed to the LUMENGT-AI STEMI detecting algorithm. Preprocessing: Detection of QRS complexes by wavelet system, segmentation of each EKG record into individual heartbeats (total dataset 27,152 beats) with fixed window of 0.4s to the left and 0.9s to the right of main QRS; Classification: A 1-D convolutional neural network was implemented, three classes were considered for individual heartbeats: “Anterior”, “Inferior” and “Lateral”, each corresponding to the heart wall affected. These individual probabilities were aggregated to generate the final label for each of the 12 leads. Training & Testing: 90% and 10% of the dataset was used respectively. Experiments: Intel PC i7 8750H processor at 2.21GHz, 16GB RAM, Windows 10 OS with a NVidia GTX 1070 GPU, 8GB RAM. Results Accuracy – Lead V2 (91.7%); Sensitivity Anterior wall – Lead V2 (97.4%); Sensitivity for Lateral wall – Lead I (10.0%); and Sensitivity for Inferior wall – Lead V2 (93.6%). Conclusions AI algorithms merged with a Single lead approach detect and localize STEMI within any setting. The V2 lead yields superior results for mapping of ischemic areas of the heart among the anterior and inferior walls. In contrast, diagnosis remains suboptimal for identifying the lateral wall. The usage of synergistic technologies facilitates easy, fast and early STEMI triage and management.