Objectives: Hyperkalemia is a common electrolyte abnormality, which in severe cases can cause fatal arrhythmia and cardiac arrest. However, it is often asymptomatic, and diagnosis is difficult without blood tests. The purpose of this study is to detect hyperkalemia quickly and easily through a deep learning-based model that has learned an electrocardiogram (ECG) that is non-invasive and can be quickly measured. Methods: Among patients who underwent an ECG at least once from 2006 to 2020, patients with blood test results within 24 hours were included, and dialysis patients were excluded. All ECGs were acquired using a GE ECG machine and the raw data (XML datatype) were stored using the MUSE data management system. For model training and evaluation, the ECG-serum K pair was separated into train, validation, and test set. Hyperkalemia and severe hyperkalemia were defined as 5.5 mEq/L ≤ K+ and as 6.5 mEq/L ≤ K+. We trained a 4-class classification model using a Convolutional Neural Network. The model input was a standard 10-second, 12-lead ECG and the output being the likelihood of the ECG being from a patient with classes of serum potassium concentration. Results: 362,896 cases of ECG-serum K were analyzed in a total of 299431 patients, of which 330,137 cases were in the train set, 15,250 cases in the validation set, and 28,783 cases in the test set. In the validation set, AUROC for hyperkalemia was 0.97 and AUPRC was 0.79, and AUROC for severe hyperkalemia was 0.99 and AUPRC was 0.86. In the test set, for hyperkalemia, the sensitivity and specificity of deep learning model were 81.1% and 80.9%. Conclusions: The deep learning model using the 12-lead ECG waveform detected hyperkalemia with high accuracy, and in severe hyperkalemia, the diagnostic predictive power was further increased. These results suggest the clinical applicability of AI software for diagnosing hyperkalemia using ECG.