Phishing attacks continue to represent one of the most persistent cybersecurity threats affecting individuals, enterprises, and digital infrastructures worldwide. Traditional rule-based and signature-based security systems increasingly struggle to detect sophisticated phishing campaigns due to the adaptive behavior of attackers, dynamic malicious URLs, and AI-enabled social engineering techniques. This study proposes a Hybrid Intelligent Framework for Detecting and Identifying Suspicious Phishing Attack Activities through the integration of machine learning algorithms, behavioral analytics, URL feature extraction, and ensemble classification techniques. The framework combines Random Forest, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) approaches to improve detection accuracy, reduce false positives, and enhance adaptive response capabilities. The research synthesizes existing studies on phishing detection, AI-enabled cybersecurity systems, and hybrid learning architectures to establish a comprehensive analytical model. The proposed methodology evaluates phishing indicators including lexical URL structures, domain characteristics, email metadata, and user behavioral responses. Findings indicate that hybrid intelligent systems significantly outperform isolated machine learning techniques in phishing detection accuracy, scalability, and resilience against evolving threats. The study further identifies the importance of integrating explainable artificial intelligence and adaptive learning mechanisms for future cybersecurity infrastructures. The research contributes a theoretically grounded and technically feasible framework suitable for real-time phishing detection environments across enterprise and cloud-based ecosystems.