This article explores the transformative potential of data science in enhancing justice systems globally. Leveraging the increasing availability of judicial data and the advancements of the digital revolution, this paper demonstrates how policymakers can significantly improve access, efficiency, and fairness within justice systems—crucial components of economic development as discussed in a companion paper (Ramos-Maqueda and Chen, 2024). We introduce a comprehensive framework for evaluating, diagnosing, and experimenting with judicial processes to deepen our understanding of judicial performance using data science methodologies. Key areas of focus include the application of machine learning and “text-as-data” techniques to enhance efficiency and identify disparities in judicial rulings. Through detailed case studies and empirical evidence, we illustrate how these technologies can address systemic shortcomings and drive meaningful reforms. By identifying specific areas where data science can bridge existing gaps, we aim to provide actionable insights for policymakers. Our findings highlight the profound impact of data-driven approaches on fostering a more just society and promoting sustainable economic growth. The paper concludes by suggesting future research directions and practical applications of data science in judicial contexts to ensure continuous improvement and innovation.