The main motor skills are impacted by Parkinson’s disease (PD), a neurodegenerative ailment that is both chronic and progressive. The signs of this condition include tremors, stiffness, bradykinesia (slow movement), and postural instability. The degeneration of brain cells that produce dopamine is the root cause of these symptoms. This study introduces an innovative algorithmic framework for predicting Parkinson’s disease (PD) using spiral drawing tests analyzed through advanced wave signal processing techniques. By collecting spiral drawings from patients via a digital tablet and converting these into wave signals, the framework employs three key algorithms: Wavelet Transform-Based Feature Extraction (WTFE), which decomposes signals into frequency bands to extract subtle features indicative of PD; a Gated Recurrent Unit with Long Short-Term Memory (GRU-LSTM) hybrid neural network that captures both short-term and long-term dependencies in the signal data to detect tremor patterns; and Hybrid Ensemble Classification (HEC), which integrates multiple classifiers—Support Vector Machines, Random Forests, and Gradient Boosting Machines—aggregated through a weighted voting scheme. Evaluated on a dataset of drawings from PD patients and healthy controls, the framework demonstrated high accuracy in distinguishing PD, suggesting it as a promising tool for early and objective diagnosis, thereby facilitating timely and effective intervention.