The CONVERGENCE OF ARTIFICIAL INTELLIGENCE AND PRECISION MEDICINE IN BATTEN DISEASE: PROGRESS FROM 2001-2026

Batten disease (neuronal ceroid lipofuscinosis; NCL) is a group of rare, fatal neurodegenerative lysosomal storage disorder marked by seizures, cognitive decline, vision loss and premature death. Batten disease has evolved substantially over the past quarter century. this review indicates the journey from initial genomic mapping to today’s landscape of AIdriven insights and large-scale therapeutic production. Batten disease undergoes into three phases: Phase 1 (2001-2010) this era identified genetic blueprint and understanding lysosomal storage mechanism. Phase 2 (2011-2020) it highlighted by the 2017 FDA approval of cerliponase alfa (Brineura) and the Adoption of retinal biomarkers such as OCT. Phase 3 (2021-2026) is characterized by the use of machine learning model like pop EVE to overcome data scarcity in rare disease research. Emerging Artificial Intelligence (AI) approaches offer transformative potential across diagnosis, biomarker discovery, progression modeling, and individualized treatment. AI enabled precision medicine framework (2001-2026), highlighting milestones in data integration, machine learning for rare disease and future direction in batten disease research and clinical care. By 2026, precision medicine has shifted from experimental to standardized, scalable models, offering global hope.