<p>To improve the efficiency and effectiveness of cloud task scheduling, hybrid optimization techniques are gaining significant traction. This paper presents an in-depth analysis of hybrid optimization methods used in cloud task scheduling, with a focus on resource management metrics, scalability, the handling of fluctuating workloads, and optimization goals. The study methodically details the research approach and offers a state-of-the-art survey of existing hybrid optimization techniques. Subsequently, the paper provides a detailed assessment and performance comparison of these methods, highlighting their relative advantages and disadvantages. This paper centers on the various hybrid scheduling methodologies currently being utilized in cloud computing, particularly those employing multi-objective optimization. We categorize current approaches, explore relevant research problems, and identify significant unresolved challenges within this field. The analysis reveals that Meta Hybrid approaches account for 38% of studies, making them the most widely adopted. Following this, Hyper Hybrid approaches (25%) are emerging as a strong contender, integrating AI-driven techniques for real-time adaptability. The research also highlights that 15% of studies prioritize makespan minimization, underscoring its importance in cloud performance optimization. Although energy efficiency is acknowledged as important, it receives relatively less direct attention in the reviewed body of literature (3%). Moreover, CloudSim remains the dominant evaluation tool (37%), reinforcing its role as a benchmark for hybrid scheduling research. The findings emphasize that future advancements should focus on AI-driven hybrid scheduling, energy-aware models, and multi-objective optimization to improve adaptability, scalability, and sustainability in cloud environments. We discuss these critical challenges and potential research directions that help in efficient cloud scheduling. </p>