Introduction/Objective:: Parkinson's disease (PD), Huntington's disease (HD), amyotrophic lateral sclerosis (ALS), and spinocerebellar ataxias (SCAs) are examples of neurodegenerative disorders (NDDs) that share overlapping neuropathological processes and largely affect motor coordination. For early diagnosis, illness monitoring, and treatment targeting, it is essential to find trustworthy biomarkers that represent motor circuit dysfunction. The purpose of this study is to summarize the state of the art regarding molecular, neurochemical, and imaging biomarkers that are pertinent to motor impairment and to investigate the function of artificial intelligence (AI) in their identification and verification. Methods:: With an emphasis on biomarker discovery, validation, and AI/ML applications in PD, HD, ALS, and SCAs, a thorough literature search was carried out in the PubMed, Scopus, and Google Scholar databases for research published between 2015 and 2025. The motor-specific correlations of key molecular (α-synuclein, tau, neurofilament light chain, TDP-43, mutant huntingtin), neuroimaging, and digital biomarkers were carefully examined. Results:: AI-driven methods, such as deep learning and machine learning, have shown great promise in combining multimodal data from digital, fluid, and imaging sources. These techniques enhanced the detection of disease-specific biomarker signatures, especially those associated with deficiencies in motor coordination. Discussion:: Data heterogeneity, biomarker standardization, model interpretability, and limited cross-disease validation are still issues despite encouraging developments. Improving the clinical reliability of AI-based biomarker models requires filling in these gaps. Conclusion:: An effective foundation for deciphering intricate motor neurological pathways is provided by AI-assisted biomarker discovery. Transparent algorithms, multicenter data integration, and ethical frameworks should be given top priority in future research to guarantee clinical translation and better patient stratification.
Pavithra et al. (2026) studied this question.