With the rapid advancement in technology, cloud computing emerged as a bulwark for addressing burgeoning demand for accelerated and efficient computation processes. Scheduling workflows in cloud environments, however, proves to be a challenging problem, due to various challenges in terms of platforms and changing demands. In braving such challenges, hybrid metaheuristic algorithms have proven to be incredibly effective. Since these algorithms utilize the best of a variety of techniques, such algorithms can achieve significant objectives such as shorter execution times (makespan), less consumption of energy, and overall cost savings. The current work delves deeper into hybrid metaheuristic techniques, discussing their theoretical basis, strengths, and weaknesses. Comparisons illustrate their performance under a range of scenarios, with efficiency, adaptability, and scalability in withstanding changing environments. Challenges faced by these algorithms, such as optimization of algorithm parameters and computational complexity, have also been identified, and avenues for improvement have been suggested. All such information aims to stimulate academia and professionals towards developing new, efficient hybrid techniques for cloud resource scheduling and allocation.