The study explores the integration of machine learning techniques (ML) to improve the composting process of organic waste, amid the growing global challenge of waste management. A decade-long bibliometric analysis (2013-2022) using the Scopus database examined the evolution and impact of ML applications on composting. Their analysis identified important contributions to research and emerging trends, highlighting the fundamental role of ML in optimizing compost production, a critical solution for managing the growing volumes of organic waste generated worldwide. The study revealed how ML models, including artificial neural networks and genetic algorithms, revolutionized composting processes by predicting, improving, and monitoring various composting parameters. In addition, the paper delved into the geographical distribution of research efforts, highlighting the dominance of countries such as China, India and the United States in this area of research. Through the analysis of 180 articles, the study not only mapped out the current picture of ML in composting, but also identified gaps and opportunities for future research. The results defended the potential of the ML to significantly improve the efficiency and effectiveness of composting operations, thus contributing to more sustainable waste management practices. This work is a fundamental resource for both researchers and professionals, with the aim of harnessing the power of the ML in favour of the environment.