Continuous blood pressure monitoring is crucial for the prevention and management of cardiovascular diseases. However, conventional cuff-based measurement methods are unsuitable for prolonged ambulatory use due to discomfort and restricted mobility. To overcome these challenges, we present MASM-Net, a deep learning architecture for accurate cuffless blood pressure estimation from single-channel photoplethysmographic (PPG) signals. The proposed model integrates three key components including stacked Adaptive Multi-Scale Convolution (AMSC) modules for comprehensive temporal feature extraction, a Dimension Expansion (DE) module for enhanced feature representation, and a Mamba module for efficient long-range temporal dependency modeling with linear computational complexity. Extensive experiments on two public datasets demonstrate that MASM-Net achieves state-of-the-art performance, with mean absolute errors and standard deviations of 2.25 ± 3.90 mmHg (systolic) and 1.26 ± 2.14 mmHg (diastolic) on the UCI dataset, and 2.69 ± 3.80 mmHg (systolic) and 1.63 ± 2.26 mmHg (diastolic) on the BCG dataset. These results surpass those of existing methods, establishing a robust framework for continual, noninvasive blood pressure monitoring.