Genomic analysis is a powerful way to understand viral pathogens and their variations. However, most of the genomic analysis methods are based on sequence alignment, which has a high computational cost. This study introduces a novel methodology to extract discriminative regions from viral genomes. Using exclusive k-mers through strategically defined sliding windows, our approach identifies genomic regions with high concentrations of variant-specific signatures, showcasing high-accuracy classification while requiring modest computational resources. The data-driven and nonparametric nature of our approach enables pattern extraction without imposing predefined distributions, enhancing both analytical flexibility and result interpretability. By balancing minimal k-mer sizes with maximum discriminative power, our method achieves remarkable generalization capability even with limited training samples. The computational efficiency of the methodology alongside the biological transparency and explainability in the results makes it accessible to research environments with restricted processing capacity, potentially accelerating genomic signature discovery across diverse viral pathogens and contributing to better variant tracking and characterization, thus opening up even more possibilities in genomic analysis studies.