Introduction: Heart failure (HF) is a global pandemic with an increasing prevalence. The burden of HF-related hospitalizations and costs are increasing. Artificial intelligence(AI) algorithms applied to electrocardiograms have shown promise in diagnosing HF, but they require a large amount of training data, which is scarce and costly. Transfer learning addresses this challenge by utilizing knowledge from previous tasks, resulting in superior performance with limited data compared to conventional AI algorithms Hypothesis: AI algorithms using transfer learning can be accurately diagnose heart failure with reduced ejection fraction(HFrEF) using electrocardiograms compared to conventional AI algorithms. Methods: All patients aged 18 years older from the two hospitals with at least one ECG were included in the study. Electrocardiogram, transthoracic Echocardiogram (TTE), and demographic data were collected. The AI algorithm consisted of two phases: conventional AI algorithms, pre-training with a large-scale ECG dataset and transfer learning for HFrEF detection. Performance evaluation was conducted using various metrics, including AUROC, sensitivity, specificity, accuracy, F1 score. Results: The dataset consisted of 687,911 ECGs from 259,943 patients. Testing the conventional model on the HFrEF resulted in a sensitivity of 63.8%, specificity of 96.2%, accuracy of 93.6%, area under the receiver operating curve (AUC) of 0.93 (95% CI, 0.92 - 0.94), and F1 score of 61.6%. Testing the transfer learning model on the HFrEF increased the sensitivity of 75.1%, accuracy of 93.7%, area under the receiver operating curve (AUC) of 0.952 (95% CI, 0.93 - 0.96), and F1 score of 65.6%. but lowered the specificity of 95.3% Conclusions: A New AI-enabled ECG algorithm for identifying patients with HFrEF can be more accurately diagnosed than conventional AL algorithms.