This thesis presents a systematic review of the literature on scheduling algorithmsin fog computing, focusing on Hybrid Adaptive Scheduling Algorithms. The challengesof scalability, energy efficiency, flexibility, and latency have been addressedin this study by evaluating the efficacy of HASA against classical approaches likeRule-Based and AI-Driven Algorithms within resource-constrained dynamic environmentssuch as healthcare monitoring, smart cities, and industrial applications.Through a comparative analysis, HASAs demonstrate superior performance by balancingadaptability, energy optimization, and task distribution. Their lightweightAI models, together with a decentralized architecture and predictive mechanisms,can enable ultra-low latency with efficient resource management, hence becominghighly suitable for delay-sensitive applications. This work also investigates a fewother strategies, such as hierarchical scheduling architectures and adaptive learningmechanisms, which would enhance the robustness and applicability of HASAs to awide range of fog computing scenarios. The results provide a foundational frameworkfor improving scheduling methodologies in fog computing and point toward futureenhancements for scalable and efficient fog computing systems.