In last-mile delivery logistics, drivers often choose routes based on personal preferences, favoring familiar roads over the shortest distance. This study proposes an innovative approach to learning drivers' routing preferences by integrating Adaptive Large Neighborhood Search (ALNS) with a sampling technique and a Machine Learning (ML)-based optimization technique. Our method, validated with real-world data, offers superior solutions that align with drivers' preferences, advancing ML-powered last-mile delivery planning.