Journal of Networking Technology Nguyen Minh Tuan 17 3 2026 https://doi.org/10.6025/jnt/2026/17/3/161-180 https://www.dline.info/jnt/fulltext/v17n3/jntv17n3_3.pdf The rapid evolution of the Internet of Vehicles (IoV) introduces significant challenges for dynamic routing due to high mobility and complex interdependencies among network parameters. Datasets used for machine learning based routing decisions frequently exhibit multicollinearity, leading to unstable model estimates, inflated variances, and reduced interpretability. Traditional mitigation techniques, such as Principal Component Analysis, often sacrifice valuable domain specific information by transforming or eliminating variables. This study proposes a comprehensive framework combining extensive multicollinearity diagnostics with a Ridge Classifier to enhance routing decision making in dynamic IoV networks. Using a synthetic dataset of 4,000 instances comprising 28 numerical features, we performed rigorous diagnostics including Variance Inflation Factor (VIF), condition indices, and Belsley analysis confirming severe feature interdependencies. Instead of discarding variables, we applied L2 regularization via a Ridge Classifier to stabilize coefficient estimates while preserving all domain relevant predictors. The optimized Ridge model achieved a test accuracy of 84.63% and a 5-fold cross validation accuracy of 84.50%, significantly outperforming a baseline Random Forest classifier at 81.13%. Additionally, implementing class weights successfully improved minority class recall, highlighting the framework's adaptability to imbalanced routing scenarios. Ultimately, the empirical results Proved that ridge regularization effectively mitigates numerical instability caused by multicollinearity without sacrificing critical vehicular network metrics. This research provides a reliable, interpretable, and computationally efficient analytical foundation for intelligent routing protocols, contributing to more resilient next generation intelligent transportation systems. Future work will explore real world deployment and advanced class balancing strategies.