It is a well-known fact that it is important to accurately measure blood pressure. In particular, it is an even more important vital sign for the elderly and patients in hospitals. Non-invasive techniques for blood pressure measurement only afford discrete results. Although invasive measurement techniques overcome this issue and provide continuous results, they involve the risk of bleeding or infection and also cause discomfort to the patient. To address this issue, in this study, we developed a deep learning model that can estimate the arterial blood pressure (ABP) in real time using the waveform signals from electrocardiograms (ECGs) and photoplethysmograms (PPGs), without handcraft setting. Data pertaining to patients with various disorders admitted in the intensive care unit (necg, ppg= 1,126,870) were used. The performance of the model was evaluated (RSBP= 0.96, RMAP=0.92, and RDBP = 0.90) and verified to meet international standards. Even if the actual value changes dramatically, the estimated value shows a graph that follows the trend. Additionally, the ABP of patients with atrial fibrillation could be measured continuously and in real time.