Artificial intelligence (AI) is rapidly advancing our ability to identify and interpret genetic variants associated with coagulation factor deficiencies. This review introduces AI, with a specific focus on machine learning (ML) methods, and examines its applications in the field of coagulation genetics over the past decade. We observed a significant increase in AI-related publications, with a focus on hemophilia A and B. ML approaches have shown promise in predicting the functional impact of genetic variants and establishing genotype-phenotype correlations, exemplified by tools like "Hema-Class" for FVIII variants. However, some challenges remain, including the need to expand variant selection beyond missense mutations (which is now the standard of most studies). For the future, the integration of AI in calling, detecting, and interpreting genetic variants can significantly improve our ability to process large-scale genomic data. In this frame, we discuss various AI/ML-based tools for genetic variant detection and interpretation, highlighting their strengths and limitations. As the field evolves, the synergistic application of multiple AI models, coupled with rigorous validation strategies, will be crucial in advancing our understanding of coagulation disorders and for personalizing treatment approaches.