Parkinson’s disease remains a major clinical issue in terms of early detection, especially during its prodromal stage when symptoms are not evident or not distinct. To address this problem, we proposed a new deep learning 2-based approach for detecting Parkinson’s disease before any of the overt symptoms develop during their prodromal stage. We used 5 publicly accessible datasets, including UCI Parkinson’s Voice, Spiral Drawings, PaHaW, NewHandPD, and PPMI, and implemented a dual stream CNN–BiLSTM architecture with Fisher-weighted feature merging and SHAP-based explanation. The findings reveal that the model’s performance was superior and achieved 98.2%, a F1-score of 0.981, and AUC of 0.991 on the UCI Voice dataset. The model’s performance on the remaining datasets was also comparable, with up to a 2–7 percent betterment in accuracy compared to existing strong models such as CNN–RNN–MLP, ILN–GNet, and CASENet. Across the evidence, the findings back the diagnostic promise of micro-tremor assessment and demonstrate that combining temporal and spatial features with a scatter-based segment for a multi-modal approach can be an effective and scalable platform for an “early,” interpretable PD screening system.