Research on Game Theory-based Pricing and Capacity Planning in Cloud Computing focuses on applying strategic decision-making models to optimize pricing strategies and resource provisioning in dynamic, multi-tenant cloud environments. This area investigates how cloud providers and consumers interact in competitive or cooperative settings to achieve cost efficiency, revenue maximization, and service-level compliance. Key research directions include designing non-cooperative and cooperative game models for dynamic pricing and capacity allocation, auction-based mechanisms for on-demand resource pricing, and Stackelberg games for hierarchical decision-making between providers and tenants. Other emerging topics involve mechanism design for incentive-compatible and fair pricing, evolutionary games for adaptive capacity planning under fluctuating workloads, and Nash equilibrium-based approaches for stable pricing and resource distribution. Additionally, integrating predictive analytics and machine learning with game-theoretic models for anticipatory pricing and demand forecasting, energy-aware capacity planning, and multi-cloud or hybrid-cloud scenarios are promising avenues for future research.