Efficient task scheduling is pivotal in cloud computing, especially for Big Data processing. This study explores task scheduling's significance, focusing on energy efficiency. Leveraging a Modified Gravitational Search Algorithm (MGSA), it surpasses existing optimization techniques, showcasing superior cost reduction and energy efficiency in cloud environments. Future research avenues include integrating machine learning for algorithm adaptability, exploring edge and quantum computing impacts, and addressing real-world scalability challenges. This study provides a foundation for advancing task scheduling algorithms, offering insights into optimizing resource allocation for diverse cloud computing scenarios.