This book is focused on enhancing the endurance of Non-Volatile Random Access Memory (NVRAM) for embedded systems applications. It describes the methodology that combines optimized machine learning algorithms based on workload prediction and data compression techniques to prolong the lifespan of NVRAM. The framework utilizes an Instruction Per Cycle-based Dynamic Pattern Compression model to analyze and compress workloads, as well as a Workload Hybrid Energy Adaptive Learning model to categorize and further compress data for storage. The book provides a solution for improving NVRAM endurance, which is crucial for the performance of embedded devices, by addressing workload prediction and efficient compression.