Continuous and non-invasive blood pressure (BP) monitoring is a challenging problem, as typical methods are intermittent, not suitable for continuous use, and often require either clinical supervision or user interaction. In recent years, machine learning and deep learning approaches have been employed for this task, achieving promising results using photoplethysmography (PPG), but often at the cost of high model complexity and limited suitability for low-power or embedded deployment. Spiking Neural Networks (SNNs), inspired by biological neural processing, offer an interesting alternative due to their event-driven nature and potential for energyefficient implementation on neuromorphic hardware. In this work, a lightweight SNN architecture for BP estimation is proposed and evaluated across four public datasets: BCG, Sensors, PPGBP, and UCI.