Journal of Networking Technology Dit Suthiwong 17 3 2026 https://doi.org/10.6025/jnt/2026/17/3/136-160 https://www.dline.info/jnt/fulltext/v17n3/jntv17n3_2.pdf The evolution of network management toward user centric Quality of Experience (QoE) necessitates intelligent routing frameworks capable of balancing multimedia quality, communication reliability, and energy efficiency in dynamic environments such as fifth generation Vehicular Ad Hoc Networks (5G VANETs). This study presents an explainable machine learning approach to analyze and predict QoE-aware energy-efficient routing decisions using a simulated dataset of 10,000 routing instances encompassing network, multimedia, and energy related parameters. A Random Forest classifier with balanced class weighting was employed, achieving near perfect predictive performance with 99.9% accuracy, 99.56% balanced accuracy, and a Matthews Correlation Coefficient of 0.998. To address the "black box" nature of machine learning, SHapley Additive exPlanations (SHAP) provided both global and local interpretability, consistently identifying packet delay and video quality (PSNR) as the dominant determinants of routing outcomes. High_QoE_Route instances exhibited superior performance, characterized by lower delays (32.94 ms) and higher PSNR (43.96 dB), while maintaining competitive energy consumption profiles. Statistical analysis confirmed significant differences across routing classes for delay and PSNR. The findings demonstrate that effective routing emerges from optimizing multiple performance dimensions simultaneously, where delay and perceptual quality outweigh packet loss in influencing user satisfaction. By integrating high predictive accuracy with model transparency, the proposed framework enhances trustworthiness and practical deployability, contributing toward more reliable, sustainable, and intelligent vehicular communication systems for next generation transportation infrastructures.