The integration of Artificial Intelligence (AI) into judicial systems is reshaping legal processes by improving efficiency, reducing human biases, and enabling data-driven decision-making. This paper provides a comprehensive analysis of AI applications in the judiciary, focusing on predictive justice, case management automation, legal research, and ethical implications. Through detailed case studies from the U.S., EU, India, Brazil, and China, we evaluate the effectiveness and challenges of AI in courts. The study employs empirical data, comparative legal analysis, and policy evaluations to assess AI’s role in enhancing judicial transparency and fairness. Recommendations for responsible AI adoption, including bias mitigation and regulatory frameworks, are discussed. The integration of Artificial Intelligence (AI) into judicial systems represents a significant evolution in legal administration, fundamentally transforming traditional processes through enhanced efficiency, objectivity, and data-driven insights. By leveraging advanced computational capabilities, AI applications in the judiciary are addressing long-standing challenges such as case backlog, inconsistent rulings, and resource constraints while simultaneously introducing new considerations regarding fairness, accountability, and ethical governance. This paper presents a comprehensive investigation into the multifaceted role of AI in judicial systems, with particular emphasis on three critical domains: predictive justice systems that analyze historical case data to forecast legal outcomes, automated case management platforms that streamline court operations, and AI-powered legal research tools that augment judicial decision-making with comprehensive precedent analysis. Through an extensive examination of international implementations, including the United States' COMPAS recidivism algorithm, Brazil's Victor AI case management system, India's SUPACE judicial research assistant, China's Smart Court initiative, and the European Union's e-Justice platform, this study provides a comparative assessment of AI's operational effectiveness across diverse legal traditions and institutional frameworks. The research methodology incorporates quantitative analysis of judicial efficiency metrics, qualitative evaluation of ethical implications, and systematic review of policy responses to AI integration in justice administration. Key findings reveal that while AI technologies demonstrate substantial potential to reduce case processing times by up to 40% in some jurisdictions and improve consistency in judicial outcomes, they also present significant challenges including the perpetuation of historical biases, lack of algorithmic transparency, and potential erosion of judicial discretion. The paper further examines contemporary debates surrounding the appropriate balance between technological efficiency and fundamental judicial values, proposing a set of evidence-based policy recommendations for responsible AI adoption. These include the development of standardized bias assessment protocols, implementation of explainable AI frameworks tailored to judicial contexts, and establishment of hybrid decision-making systems that preserve human oversight while benefiting from computational analysis.