Cardiovascular diseases (CVDs) are the leading cause of global morbidity and mortality, necessitating the precise and continuous monitoring of blood pressure for proactive management. Our study presents the AMRUNet: a novel network designed exclusively for PPG-only, noninvasive, cuff-less blood pressure estimation. The network innovates upon the U-Net architecture, integrating a MultiRes Block for detailed multi-scale feature fusion and a residual block to mitigate the issue of vanishing gradients. An attention mechanism is further employed to selectively enhance salient features within the PPG signal. Our PPG-only AMRUNet demonstrates exceptional performance in translating PPG data into accurate ABP waveforms, achieving mean absolute errors (MAE) that comply with the standards of both the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). Our method demonstrates highest MAE for both systolic blood pressure (SBP) and diastolic blood pressure (DBP), achieving a 2.85 MAE for SBP and 1.79 MAE for SBP among competing models. The model’s proficiency in precisely estimating systolic and diastolic blood pressure, along with its ability to reconstruct continuous ABP waveforms, contributes to reliable and trustworthy medical decision-making systems. The code for AMRUNet can be accessible at https://github.com/ijcnn2024/AMRUNet.