Current cuff-less blood pressure (BP) monitoring methods have significant prediction errors, especially in the hypertensive population. Therefore, we proposed a two-stage framework based on the photoplethysmography (PPG) signal, termed SMART-BP, which can independently model in different BP intervals (e.g., hypotensive, normotensive, and hypertensive), thereby achieving high-precision noninvasive BP measurement. Specifically, SMART-BP utilizes a two-stage framework for noninvasive BP measurement based on PPG signals. The first stage involves a deep learning model (named SEM-ResNet) that identifies the BP interval, referred to as the coarse-grained classification phase (CCP). The second stage employs an automated machine learning (AutoML) pipeline to estimate the BP in the corresponding interval, referred to as the fine-grained regression phase (FRP). To improve the BP prediction accuracy, we designed a PPG morphological feature learning (PMFL) algorithm to obtain a highly correlated feature subset. These discriminative features can be used as prior knowledge for coarse-grained classification networks and to provide stable results for fine-grained regression pipelines. We further demonstrated the robustness of the proposed approach using two independent multicenter datasets by employing transfer learning techniques. We first fine-tuned SMART-BP with a large high-quality dataset (Mindray dataset) and then transferred the pretrained model to the target domain (MIMIC dataset). Experimental tests showed that the SMART-BP had an estimation error of −0.01 ± 1.85 and 0.01 ± 3.50 mmHg for diastolic and systolic BP (SBP), respectively, which met the advancement of the medical instrumentation standard. These results demonstrated the high reliability and robustness of the SMART-BP in measuring BP values.