The issue of load balancing has become central to the sphere of cloud computing, mostly because there exist seemingly conflicting goals like making the makespan minimal, eliminating the energy usage, maximizing the utilization of resources, and restraining the disproportion of the virtualized resources. Against this backdrop, the current paper outlines a hybrid meta-heuristic based framework of scheduling whereby integrating Chaos-enhanced Black Widow Optimization (CBWO) with Genetic Algorithms (GA) operators in a synergistic manner can significantly enhance the multi-objective performance in the context of heterogeneous clouds. The offered CBWO hybrid model takes advantage of chaotic perturbations as a means of intensifying the exploration of solution space, and the GA-based crossover mechanisms are used to explore good areas and increase the stability of solutions. This two-pronged approach allows making a more proportional trade-off between competing goals. Empirical test results on the CloudSim simulation framework, including increasing workload conditions, have shown that the CBWO-GA configuration provides a 25-30% decrease in makespan, an 80-85% resource utilization rate, and a near-optimal load-imbalance value of 1.1, outperforming the CBWO-only and CBWO-SA configurations. These results conclusively confirm the effectiveness of the hybrid CBWO-GA design in the modern data center that needs scalable, balanced, and energy-sensitive scheduling designs. The findings highlight the promise of using chaotic dynamics combined with evolutionary operators to tackle the problem of complex multi-objective optimization in cloud computing.