Phase-Change Memory (PCM) is a non-volatile memory technology that leverages the phase transitions of chalcogenide glass for scalable storage. While it offers low power leakage and cost-efficient read operations, it faces significant challenges due to high write energy consumption and limited endurance ( \(10^7\) to \(10^9\) cycles). To address these limitations, we propose a Neural Network (NN)-based data approximation framework that classifies energy levels and selectively approximates high-energy bit patterns in multi-level cell (MLC) PCM. By employing dynamic approximation techniques, our approach effectively reduces high-energy writes, leading to lower overall energy consumption. A flag byte is embedded in the least significant byte of the approximated data, enabling a Convolutional Neural Network (CNN) to accurately reconstruct the original data. This methodology enhances PCM endurance and mitigates write energy constraints while ensuring data integrity. By integrating intelligent approximation and retrieval mechanisms, our framework improves PCM’s viability as a primary memory solution, addressing critical challenges in energy efficiency and longevity.