Intelligent, dependable, and explainable computational methods for brain disease datasets remain underdeveloped and insufficiently validated, despite significant progress in machine learning (ML). Addressing this gap is crucial, as neurodegenerative diseases (NDs), such as Parkinson’s disease (PD), continue to affect an increasing number of individuals. Given the challenges in managing and accurately detecting NDs, this thesis explores three approaches for data collection and analysis. Specifically, it examines diffusion tensor imaging (DTI), eye tracking, and online cognitive testing, along with selected ML algorithms designed to model PD patterns within these datasets. In the first approach, ML identified brain abnormalities through DTI analysis, detecting issues such as handwriting distortion after deep brain stimulation treatment with 82% accuracy. In the second approach, ML analyzed rapid eye movements (saccades) to identify patterns that differentiate PD patients. By combining eye-tracking data with ML algorithms, models predicted symptom development using Unified Parkinson’s Disease Rating Scale (UPDRS) scores with 57–79% accuracy. In the third approach, data from web-based cognitive and behavioral tests was used to identify motor and cognitive signs of PD. Using this dataset, ML distinguished healthy controls (HC) from PD patients with 93% accuracy and mild PD from advanced PD with 80% accuracy (based on their UPDRS score). Although limited by sample size, each method provided computational insights into disease progression and helped determine PD severity based on digital data and machine learning models. Thus, the findings of this thesis contribute to the field of computer science by designing, developing, and describing digital platforms and explainable ML workflows, which are applicable to complex and limited datasets.