Cities facing increased waste production face substantial environmental difficulties related to organic waste management systems. Traditional organic waste decomposition techniques achieve their objectives through painstaking processes yet create numerous decomposition byproducts. This study explores the capabilities of AI-enabled composting solutions reinforced by both machine learning systems and IoT sensor networks to manage waste decomposition in real time. The main goal aims to boost compost decomposition performance while decreasing its environmental stressors through precise condition management. Live monitoring of key compost parameters including temperature, moisture, pH levels, and microbial activity uses Internet of Things sensors in this research. Through the analysis of collected data, machine learning algorithms adjust conditions automatically to support efficient decomposition operations. Data from experiments proves that AI implementation in composters’ results in faster decomposition time rates than conventional methods with superior process performance improvements. The AI system delivers continuous operational adjustments, which keeps waste quality high while minimizing both physical space usage and waste products, thus improving sustainability in waste management. This system reduces the greenhouse gas emissions that result from organic waste breakdown activities. The research reveals how artificial intelligence-based composting functions fundamentally as an essential tool for bettering waste management systems along with environmental sustainability efforts. The study ends with proposals for additional research, which aims at perfecting AI models that function across different composting scenarios and expanding their potential in waste management infrastructure.