The increasing workloads on AI and ML, at scale, have revealed some of the largest performance limitations in the Java Virtual Machine (JVM), which is a popular runtime platform used in Java applications. The paper explores the problems and opportunities of improving the performance of the JVM in the case of resource intensive AI/ML applications. The paper discusses the weaknesses of JVM in terms of memory management, garbage collection, and parallelism that limit its effectiveness in large machine learning models and real-time data processing. By utilizing a mixed-methods research method, the article analyzes various optimization strategies, including memory allocation, concurrency models, and hardware integration. A case study on performance benchmarking reveals significant improvements in processing speed, memory efficiency, and scalability, all of which are targeted JVM enhancements. The comparative analysis of native and GPU-implemented frameworks highlights the potential of the JVM in AI/ML-based applications, identifying possible areas for future exploration. The paper is finalized with practical recommendations on how JVM performance can be optimized in AI/ML settings, and what directions may be pursued further in that regard.