INTRODUCTION: Parkinson's Disease (PD) is a progressive neurodegenerative disorder characterized by the depletion of dopaminergic neurons and the buildup of α-synuclein aggregates, resulting in damaging motor and non-motor symptoms. Conventional therapies, comprising levodopa and dopamine agonists, give symptomatic relief but fail to terminate disease progression and are associated with long-term complications. METHOD AND MATERIALS: Relevant review papers and articles from the past were investigated. Certain factors, including early disease diagnosis, therapeutic efficacy in PD models, and predictive modeling of drug-nanoparticle interactions, were considered during the conduct of this research. This literature review is a comprehensive narrative of research articles obtained from various platforms, namely Scopus, PubMed, Google Scholar, and Research Gate. Inclusion and exclusion criteria were applied to filter out the suitable materials. RESULT: Artificial intelligence (AI) is emerging as a complementary tool, facilitating design-optimized nanocarriers and predicting drug interactions while emphasizing liposomes and metallic nanoparticles as important platforms for dopamine replacement, gene therapy, and neuroinflammation modulation. DISCUSSION: Despite the progress made so far, clinical translation still has considerable challenges to overcome, including nanoparticle toxicity, scalability, long-term safety, and variability in AI model performance. Integration of AI with biologically relevant PK/PD models and personalized nanomedicine strategies should overcome such existing gaps and enhance the therapeutic reliability of nanomedicine. CONCLUSION: This review summarizes current advances in nanomedicine and AI-driven approaches for PD, discussing their mechanisms, therapeutic targets, and future perspectives in achieving disease-modifying interventions.