<p>In the cloud computing platform, workflow scheduling is a requisite element for enhancing the system performance and efficient resource management. When a set of requests or tasks reaches the cloud, the system is intended to respond to each of them to minimize the execution time. However, an excessive number of requests at once led to an unbalanced load of resources. This can be overcome by using an appropriate load-balancing strategy. This paper introduced a novel method named Enhancing Modified Min-Min using Cuckoo Search Algorithm (EMMCA) in Cloud Computing, inspired by the brood parasitism behaviour of cuckoos to minimize the overall execution time and other QoS parameters. This study aims to balance the loads of virtual machines across servers and achieve the optimal makespan of the system. To assess the effectiveness of the proposed method, comprehensive simulations are conducted using five scientific workflows. The outcomes of our experiments show that our approach achieves the highest makespan reduction by ranging from 39.3% to 52.6% for the Ligo_inspiral workflow. Similarly, the highest load balancing was also reduced by ranging from 1.1% to 6.7% for the montage workflow. The results are compared with state-of-the-art algorithms and demonstrate remarkable performance in achieving makespan, load balancing, speedup, and other QoS parameters, as well as in other scientific workflows. These results are also validated using statistical testing with ANOVA.</p>