Polarimetric X-band radars offer high-resolution precipitation observations that are often challenged by attenuation, calibration errors, and absence of routine correction procedures, which limit reliable quantitative precipitation estimation (QPE). This study proposes a two-stage machine learning framework for estimating near-surface rainfall from the Cyprus national X-band radar network. In the first stage (Stage 1), feedforward neural networks correct raw ground radar reflectivity using volume-matched Ku-band measurements from the Global Precipitation Measurement (GPM) Mission dual-frequency precipitation radar (DPR). In the second stage (Stage 2), the corrected reflectivity is used as input to regression models, including support vector regression (SVR) and neural networks, to estimate rainfall rates using tipping-bucket rain gauge data. Results show that the Stage 1 networks substantially improve ground radar reflectivity, while Stage 2 SVR models outperform traditional <em>Z</em>&ndash;<em>R</em> relationships in predicting rainfall, despite residual underestimation and moderate accuracy. The study highlights the potential of machine learning methods for X-band radar QPE in environments with limited calibration and emphasizes the benefit of combining multiple radar datasets to improve spatial consistency. These findings provide practical insights for enhancing rainfall estimation in Cyprus and other regions with similar radar network constraints.