When dependability and exact timing are paramount, real-time operating systems (RTOS) are an absolute must. System speed, latency mitigation, and predictability are all greatly improved with well-managed RTOS memory. In this work, we look at the main problems with real-time operating systems' memory management, such as fragmentation, insufficient memory resources, and time limitations. To determine how different memory management strategies affect overall system performance, we investigate methods including dynamic allocation, memory partitioning, and garbage collection. We also provide novel optimisation methodologies and solutions to these problems, such as adaptive algorithms, memory pooling, and hardware-assisted management. Various techniques' benefits, drawbacks, and performance consequences are shown by this study's thorough examination and comparison of current methodologies. Our research shows that better memory management has the ability to greatly enhance RTOS performance, leading to more efficient and reliable real-time systems.