Courts in many jurisdictions, and in India in particular, face a persistent backlog that manual scheduling and file handling can-not clear. This paper reviews five recent studies that together cover the main building blocks of an AI-driven intelligent judicial case management system: rule-based case prioritisation, machine-learning prediction of case delay and duration, large-language-model (LLM) based judgment prediction, AI-assisted trial workflows for criminal cases, and public perception of automation at different stages of adjudication. The studies are compared on objective, data, method, reported results and stated limitations. On this basis a six-layer conceptual architecture is outlined, with explainability and human oversight as core layers. The review suggests that (i) prioritisation and delay prediction offer the most immediate and least contentious gains, (ii) outcome-prediction accuracies are promising but are not directly comparable across studies and are not sufficient for deployment, and (iii) public acceptance depends on keeping the judge in control of the later stages of decision-making. Open challenges include data quality, bias, explainability, legal regulation and evaluation in real courts.