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Artificial intelligence (AI) has rapidly transformed smart surveillance systems, significantly leveraging technologies such as deep learning, computer vision, and adaptive monitoring. Driven by the need for real‐time object detection, behavioral analysis, and automated alerting, AI‐powered surveillance is increasingly vital for enhancing public safety, operational efficiency, and decision‐making in fields such as security, health‐care, transportation, and environmental monitoring. The growing societal impact and complexity of surveillance necessitate a comprehensive examination of foundational methodologies, key application domains, and the technical, ethical, and operational challenges associated with these systems. Analysis reveals that although AI‐driven surveillance solutions offer improved accuracy, scalability, and responsiveness, they continue to face notable barriers including high computational demands, privacy and data quality concerns, interpretability limitations, and inadequate regulatory frameworks. The review further identifies critical emerging trends, such as sustainable AI practices, human‐AI collaborative models, and the integration of AI with the Internet of Things (IoT) and blockchain technologies, as significant influences shaping the future trajectory of the field. Addressing these challenges through ongoing research, innovation, and responsible deployment is essential to fully unlock the societal and industrial potential of AI‐enabled surveillance solutions.