Efficient task scheduling in cloud computing remains a complex challenge due to conflicting objectives including execution time, energy consumption, and monetary cost. This research presents a performance comparison of four recent hybrid metaheuristic algorithms—GA-PSO, GA-MFDO, HFPSO, and IC-BA, alongside a non-hybrid algorithm, BES, for workflow scheduling in cloud environments. The simulations were tested using CloudSim, which allows dynamic and heterogeneous workload modeling under realistic conditions. The multiobjective optimization used consists of composite fitness functions that incorporate makespan, total cost, and energy consumption. Several experiments were conducted by varying the number of cloudlets and virtual machines (VMs). The findings indicate that GA-MFDO and IC-BA provide more balanced solutions compared to the other algorithms, and HFPSO consistently performs poorly. This work contributes to the existing body of literature by investigating the effectiveness of hybrid approaches toward distributed, sustainable, and scalable cloud workflow scheduling.