Research on Cost Optimization using Game Theory in Cloud Computing focuses on applying strategic decision-making models to balance cost, performance, and resource utilization among cloud providers and consumers. This area explores how game-theoretic approaches can model interactions, competition, and cooperation in multi-tenant and multi-cloud environments to achieve optimal cost efficiency. Key research directions include designing non-cooperative and cooperative game models for resource pricing and allocation, auction-based mechanisms for workload distribution, and incentive-compatible strategies that encourage fair resource sharing. Other emerging topics involve Stackelberg games for hierarchical cloud service negotiation, evolutionary games for adaptive cost optimization, and mechanism design for truthful bidding in cloud markets. Additionally, integrating game theory with machine learning for predictive cost management, optimizing energy-aware cloud operations, and developing multi-objective optimization frameworks for balancing cost, performance, and sustainability are promising areas of future research.