Research on Game Theory-based Task Scheduling in Cloud Computing focuses on applying strategic interaction models to optimize how computational tasks are assigned and executed across distributed cloud resources. This area uses game-theoretic principles to balance competing objectives such as cost, energy efficiency, latency, and fairness among cloud users and providers. Key research directions include developing non-cooperative and cooperative game models for task allocation, auction-based task scheduling to ensure optimal resource utilization, and Stackelberg games for hierarchical decision-making between providers and users. Other emerging topics involve evolutionary and repeated games for dynamic task scheduling in fluctuating workloads, mechanism design for incentive-compatible and truthful task participation, and Nash equilibrium-based models to ensure stable scheduling outcomes. Additionally, integrating reinforcement learning with game theory for adaptive and intelligent scheduling, exploring multi-objective optimization for balancing performance and energy trade-offs, and applying game-theoretic approaches in edge–cloud and multi-cloud task scheduling environments are promising areas for future research.