The increasing digitization of financial operations within the U.S. healthcare sector has led to a rise in sophisticated cyber threats, necessitating advanced security frameworks for fraud detection and mitigation. Traditional cybersecurity approaches often struggle to keep pace with evolving threats, creating vulnerabilities in financial transactions, patient records, and insurance systems. This paper proposes a hybrid AI-driven threat hunting and automated incident response framework tailored for financial security in healthcare. By integrating deep reinforcement learning (DRL) with AI-driven cyber forensics, the system enhances early fraud detection, proactively identifies anomalies, and automates threat mitigation. The hybrid approach leverages predictive analytics, behavioral anomaly detection, and real-time data correlation to uncover hidden attack patterns across healthcare financial networks. Deep reinforcement learning models continuously adapt to emerging cyber threats, improving the accuracy of fraud detection by learning from past incidents. AI-driven cyber forensics strengthens investigative processes by autonomously analyzing transaction logs, identifying malicious activity, and providing real-time alerts for rapid response. Furthermore, the framework integrates automated incident response mechanisms, utilizing AI-driven security orchestration to contain threats with minimal human intervention. This study explores the impact of machine learning-based fraud detection, intelligent risk scoring, and adaptive security policies on healthcare financial security. Experimental evaluations demonstrate the effectiveness of the proposed framework in reducing false positives, accelerating response times, and mitigating fraudulent activities before financial damage occurs. By bridging AI, cybersecurity, and financial fraud detection, this research provides a scalable solution for enhancing the resilience of healthcare financial systems against evolving cyber threats.