The increasing sophistication of cyber threats targeting critical infrastructure particularly healthcare and financial systems in the United States demands real-time, intelligent threat detection and automated incident response beyond traditional reactive security models. This study investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques for real-time cyber threat assessment and response across healthcare Electronic Health Record (EHR) systems, medical device networks, financial transaction platforms, and banking information systems. The research evaluates predictive analytics, deep learning models, multi-agent systems, and explainable AI frameworks, including genetic algorithm-based feature selection, adaptive neuro-fuzzy inference systems, and generative AI–enabled multimodal threat detection. Using a mixed-methods approach that combines systematic literature review, experimental evaluation on healthcare and financial testbeds, comparative algorithm analysis, and practitioner validation with security operations center professionals, the study assesses detection latency, accuracy, false positive rates, computational efficiency, and response automation. Results show that real-time AI systems achieve a mean detection latency of 847 ms with 96.7% classification accuracy, outperforming signature-based systems by over 94%. Genetic algorithm optimization improves malware detection accuracy to 98.1% while reducing feature dimensionality by 67%, enabling deployment on resource-constrained devices. Multi-agent intrusion detection systems achieve a 97.4% detection rate with a 2.8% false positive rate, while explainable AI enhances operational trust and regulatory compliance under HIPAA and PCI-DSS requirements. Despite these gains, challenges remain related to concept drift, zero-day detection, computational overhead, and explainability–performance trade-offs. This study contributes a real-time AI-driven threat assessment and response framework, empirically validated benchmarks, and a practical deployment methodology tailored to regulated healthcare and financial environments.