This paper investigates the viability of crossover machine learning (ML) models in understanding complex optimization issues inside data frameworks. Crossover ML models, which combine two or more conventional machine learning strategies, are progressively utilized to handle multidimensional and energetic challenges where single-model approaches may drop brief. This think about surveys different crossover models, counting those that coordinated neural systems with developmental calculations, and their applications in regions such as supply chain administration, arrange optimization, and asset allotment. The study moreover discuss about the challenges of executing these models, such as computational requests and require for specialized information, nearby potential arrangements.