Cardiovascular diseases (CVD) are a leading cause of death, encompassing conditions like heart disease, hypertension, and diabetes, which increase the risk of myocardial infarction and stroke. Continuous monitoring of blood pressure is crucial due to its significant correlation with these conditions. However, traditional methods are often invasive, causing discomfort and potential infections. This study aims to develop a continuous, cuffless blood pressure detection system using data from approximately 9,000 patients in the MIMIC II database, totaling over 2 million records. The system utilizes photoplethysmography (PPG) to extract temporal and waveform features, with regression analysis for feature reduction. A Transformer-based deep learning model is employed to establish the relationship between PPG features and blood pressure. The mean error (ME) and standard deviation (SD) for systolic (SBP) and diastolic blood pressure (DBP) are −0.14  ±  9.05 and −1.15  ±  5.45, respectively. According to AAMI and BHS standards, diastolic pressure meets AAMI criteria, with systolic and diastolic pressures achieving Grade B and Grade a levels, respectively.