This study presents the design and implementation of an advanced IoT-enabled, cloud-integrated smart parking system, engineered to address the critical challenges of urban parking management and next-generation mobility. The proposed architecture utilizes a distributed network of ultrasonic and infrared occupancy sensors, each interfaced with a NodeMCU ESP8266 microcontroller, to enable precise, real-time monitoring of individual parking spaces. Sensor data is transmitted via secure MQTT protocol to a centralized cloud platform (AWS IoT Core), where it is aggregated, timestamped, and stored in a NoSQL database for scalable, low-latency access. A key innovation of this system is the integration of artificial intelligence (AI)-based space optimization algorithms, leveraging historical occupancy patterns and predictive analytics (using LSTM neural networks) to dynamically allocate parking spaces and forecast demand. The cloud platform exposes RESTful APIs, facilitating seamless interoperability with user-facing mobile and web applications. These interfaces provide end-users with real-time visualization of parking availability, intelligent navigation to optimal spaces, and digital payment integration, thereby minimizing search time and enhancing user convenience. From an administrative perspective, the system delivers comprehensive analytics dashboards, including heatmaps of space utilization, anomaly detection for unauthorized parking, and predictive maintenance alerts for sensor nodes. Field trials conducted across a multi-level parking facility demonstrated a 32% reduction in average vehicle search time and a 21% improvement in space utilization efficiency compared to conventional systems. The end-to-end solution adheres to robust cybersecurity standards (TLS 1.2 encryption, role-based access control) and is designed for modular scalability, supporting integration with smart city infrastructure and electric vehicle charging stations. This research establishes a scalable, intelligent framework for urban parking management, contributing significantly to reduced congestion, optimized resource allocation, and enhanced urban mobility.