With the rise of digital health technologies, smartphones have become a promising tool for non-invasive medical screening. This study introduces NeuroEnhanceNet, a deep learning architecture tailored for inertial sensor data collected during walking, enabling early detection of Parkinson’s disease (PD). The pipeline includes preprocessing (normalization, scaling, rotation) of accelerometer signals followed by NeuroEnhanceNet, which captures both long-term intra-channel patterns and inter-channel correlations. The model achieves an impressively low false negative rate of 0.053 for early-stage PD detection. Comparative analysis highlights that gait-derived digital biomarkers outperform those from resting-state and underscore the potential of smartphone-based, walk-derived data for scalable and accurate early PD screening.