Artificial intelligence (AI) has become an indispensable tool in biological research, providing powerful methodologies to analyze and interpret complex genomic data. In evolutionary genomics, AI has enabled researchers to explore species evolution, genetic diversity, and adaptive mechanisms. However, the “black box” nature of traditional AI models often hinders their interpretability, posing challenges for generating biologically meaningful insights. This review examines the current applications of AI in evolutionary genomics and highlights the transformative potential of explainable AI (XAI) in this field. By enhancing model transparency and aligning AI outputs with biological contexts, XAI can improve our understanding of evolutionary patterns across species. This review explores how XAI methodologies can elucidate phenomena in evolutionary genomics, from the generation of genomic data to applications in phylogenomics, population genomics, molecular evolution, and multiomics approaches. By addressing the interpretability limitations of conventional AI, XAI offers a path forward to more reliable and actionable discoveries in evolutionary genomics.