Neurological diseases, such as multiple sclerosis (MS), significantly affect hand function, impacting patients' independence and quality of life. The Nine Hole Peg Test (NHPT) is a standardized tool widely used to assess upper limb motor function. This paper explores the integration of artificial intelligence (AI) and machine learning (ML) in the analysis of kinematic data obtained from a digitized NHPT prototype. The digital NHPT captures detailed motion data, including timestamps for each action, movement patterns, and filling sequences, enabling advanced analyses of motor and cognitive processes. AI-driven methods, such as clustering, anomaly detection, and pattern recognition, provide innovative ways to evaluate fine motor skills, detect subtle anomalies, and monitor disease progression. The combination of enhanced data collection and AI-based analytics offers a comprehensive and objective approach to understanding hand function, supporting individualized therapy development, and improving clinical diagnostics. This integration represents a significant advancement in the evaluation and management of neurological diseases.