Locating a parking space in densely populated metropolitan areas poses a significant challenge for drivers, often resulting in severe traffic congestion. To address these challenges, innovative solutions are needed to enhance urban mobility and streamline parking efficiency. Our research aims to develop an intelligent parking space detection system utilizing the YOLO V8 algorithm for image processing. This system captures video feeds from the parking area and main gate, segmenting them into images for accurate detection and generates real-time data on available parking spaces and vehicle counts in the area. The system identifies vehicle types, such as cars or trucks, and improves by detecting disabled parking spots. It also sends notifications if unauthorized vehicles occupy these spaces. A powerful Raspberry Pi transmits this information to a cloud server using the MQTT protocol. For secure data transmission, TOKEN-based authentication is implemented, leveraging the TOKEN as both the username and password. Data is securely stored on a cloud server, with visualizations accessible via a website and mobile app. Users can monitor and analyze parking information through these platforms.