Recent research in resource pricing for profit maximization in cloud computing focuses on designing dynamic and intelligent pricing models that balance provider profitability with user satisfaction and optimal resource utilization. These studies employ game theory, auction-based mechanisms, and machine learning-driven predictive pricing to determine optimal rates for compute, storage, and network resources under varying demand conditions. By integrating resource allocation and pricing strategies, modern frameworks enable service providers to maximize revenue while maintaining fairness and Quality of Service (QoS). Adaptive and multi-tier pricing approaches also consider user behavior, workload variability, and energy costs, leading to more sustainable and profit-oriented cloud management systems.