Accurate identification of line-of-sight (LOS) and non-line-of-sight (NLOS) conditions is a critical challenge in wireless communication, particularly the vehicle-to-vehicle (V2V) channels, owing to the high dynamics inherent to V2V environments. This study explores the use of machine learning (ML) techniques for LOS/NLOS classification based on wireless channel features extracted from both the measured received power level and the difference between the measured attenuation and free space loss (FSL). Feature correlation, importance analysis, and cumulative scoring are applied to derive a compact and informative feature set. A performance analysis with varying numbers of input features, along with an ablation study, showed that reliable classification can be maintained even with a reduced feature set. In addition, per-class evaluation is performed to capture distinct LOS and NLOS behaviors, which is particularly relevant when prioritizing specific outcomes such as minimizing false LOS or false NLOS detection. Balanced datasets were created through random sampling from the LOS and NLOS classes, with training and testing performed using K-fold cross-validation (CV). Three models, random forest (RF), support vector machines (SVM), and LogitBoost, were evaluated based on recall, specificity, precision, and the F1-score metric. The results indicate that the RF model outperforms the others and that a minimal yet balanced training dataset from both the LOS and NLOS classes is sufficient to achieve high classification reliability. The proposed method offers interpretability, a low computational load framework, and flexibility to address specific classification needs. This aspect provides valuable insights into improving the performance of vehicular communication systems.