This video addresses the persistent challenges Indonesia faces in crime forecasting and judicial resource management, compounded by chronic underreporting and inconsistent spatial resolution in official crime statistics. In this video, a multi-level spatio-temporal machine learning framework is developed and applied to 95,666 adjudicated crime records from the Supreme Court of Indonesia spanning January 2023 to June 2024. This video follows the CRISP-DM methodology, where a hybrid STL-XGBoost v. 3.2.0 model is trained on a chronological split to forecast daily judicial caseloads, achieving an R2 of 0.8070, MAE of 16.52, and sMAPE of 9.76% on the held-out test set. This video then applies DBSCAN spatial clustering, parameterized via k-distance plot analysis (ϵ=0.3∘, 𝑚𝑖𝑛𝑃𝑡𝑠=3) and validated through Jaccard Similarity Index sensitivity analysis, identifying 29 distinct adjudicated crime hubs concentrated along Java and Sumatra’s urban and transit corridors. This video also presents a comparative analysis of reported versus adjudicated crime data, revealing systematic judicial funnel attrition ranging from 199.12% in Riau to 2436.02% in Papua, establishing that adjudicated crime records provide a reliable indicator of judicial workload rather than a comprehensive measure of social deviance. Key limitations acknowledged in this video include the 18-month observation window that may not capture long-term policy shifts and the use of city centroids as spatial proxies that introduces a degree of ecological fallacy. This video concludes that the framework offers a scalable, interpretable decision support tool for evidence-based judicial resource planning across national, provincial, and city scales in Indonesia.