The rapid evolution of smart cities has transformed the concept from a visionary idea into a practical reality through the integration of advanced digital technologies within urban infrastructure. These initiatives seek to enhance quality of life, promote sustainability, and improve the efficiency and responsiveness of public services. Among the enabling technologies, video surveillance plays a critical role in fostering safer, more secure, and better-managed urban environments. This study investigates how municipal authorities can strategically deploy video surveillance systems to accelerate smart city development, with a particular focus on urban planning, public safety, and operational efficiency. An algorithmic approach is proposed to determine optimal locations for closed-circuit television (CCTV) camera installation, aiming to maximize coverage while minimizing redundancy and cost. Furthermore, the study explores the integration of Machine Learning, a key branch of Artificial Intelligence (AI), to enhance surveillance capabilities through automated data analysis, pattern recognition, and adaptive learning. These intelligent systems enable real-time monitoring, support informed decision-making, and facilitate proactive security measures. The findings highlight the relevance of intelligent video surveillance as a foundational component of smart cities, contributing to improved governance, enhanced public safety, and sustainable urban management.