Moth bean [Vigna aconitifolia (Jacq.) Maréchal] is a climate-resilient, underutilized legume crop predominantly cultivated in the arid and semiarid regions of the Indian subcontinent. Valued for its remarkable drought tolerance and adaptability to harsh agroclimatic conditions, moth bean contributes significantly to the nutritional security of resource-poor populations in rainfed systems. Its seeds are rich in protein, dietary fiber, essential minerals, and vitamins, making it a promising crop for combating malnutrition. Despite these valuable attributes, moth bean continues to be neglected in mainstream crop improvement programs, primarily due to its low productivity. The major constraint hindering productivity is the lack of high-yielding, disease-resistant, and widely adapted varieties. Early genetic improvement efforts largely focused on phenotypic selection from existing germplasm, which provided only incremental progress. Subsequent breeding programs introduced short-duration varieties developed through induced mutagenesis. However, the crop has so far missed out on the benefits of modern genomic tools that have transformed breeding strategies in other legumes. With the recent availability of transcriptomic and whole-genome data in moth bean and closely related Vigna species, there is now an unprecedented opportunity to accelerate genetic improvement using molecular markers and genome-wide association studies. The integration of multiomics technologies and genome editing with machine learning and systems biology approaches offers unprecedented potential to dissect the genetic architecture of complex traits such as yield, abiotic stress tolerance, early flowering, and nutritional quality. These cutting-edge strategies will accelerate the development of high-yielding, climate-resilient moth bean varieties, thereby promoting its broader adoption in future-oriented, sustainable food systems.