The application of affordable and energy-efficient smart security systems has gained significant attention in recent years. This research presents a novel approach to address the space and power consumption challenges associated with conventional CCTV-based monitoring systems. By leveraging the capabilities of Internet of Things (IoT) networks and incorporating Passive Infrared (PIR) sensors, an innovative smart security system is developed. The proposed system focuses on motion detection as a trigger for activating the camera. When the PIR sensor detects motion in the surroundings, it initiates the recording process, enabling efficient utilization of resources. The system intelligently switches off the camera during periods of inactivity, effectively reducing power consumption. Furthermore, a two-tiered storage strategy, combining local storage and cloud storage, optimizes storage utilization and facilitates remote accessibility. Python programming plays a crucial role in enabling seamless communication and data transfer within the system. It efficiently manages the recording, storage, and transfer of video files to the cloud, enhancing accessibility and remote monitoring capabilities. The evaluation of the system's performance reveals significant improvements in energy efficiency compared to conventional CCTV systems. The proposed system demonstrates varying power consumption ranging from 60mW to 140mW, significantly lower than the constant power consumption of 160mW in conventional systems. This reduction in power consumption is attributed to the intelligent activation of the camera based on motion detection. This research showcases the potential of affordable and energy-efficient smart security systems for IoT networks. By addressing the limitations of conventional systems, it contributes to the development of sustainable and cost-effective security solutions. Future research can focus on further optimization of power consumption, advanced motion detection algorithms, and integration of user-friendly interfaces and analytics for enhanced security effectiveness