The increasing integration of artificial intelligence into legal and justice systems has generated renewed interestin data driven decision support, procedural efficiency, and improved access to justice, particularly withindeveloping and transitional legal environments. This study examines the emerging landscape of applied AI justicedelivery and autonomous legal decision-making frameworks, with a focus on governance, confidentiality,transparency, and human oversight. Using a qualitative doctrinal and policy analysis supported by comparativeevidence from existing judicial digitization initiatives, the paper evaluates how intelligent systems can supportcourts without undermining due process, accountability, or public trust. Findings indicate that jurisdictionsadopting structured digital workflows and AI assisted legal analytics report average reductions in case processingtime ranging between 30% and 45%, while administrative backlog reduction reaches approximately 40% in earlystage implementations. The surveyed judicial administrators and legal professionals show that 65% associate AIassisted tools with improved procedural efficiency, 58% report enhanced consistency in case management, and52% identify improved access to justice through digital platforms. However, only 44% express confidence incurrent data protection safeguards, highlighting persistent risks related to confidentiality, cybersecurity, andalgorithmic opacity. The paper contributes to ongoing academic and policy debates by articulating a governancegrounded framework for AI assisted justice delivery that prioritizes confidentiality, transparency, and ethicalaccountability. The findings provide practical insights for courts, policymakers, and legal institutions seeking tomodernize justice systems while safeguarding fundamental legal principles.