Waste transport management is a critical sector where maintaining accurate records and preventing fraudulent or illegal activities is essential for regulatory compliance, environmental protection, and public safety. However, monitoring and analyzing large-scale waste transport records to identify suspicious patterns or anomalies is a complex task. These records often involve multiple entities and exhibit variability in waste flows between them. Traditional anomaly detection methods relying solely on individual transaction data, may struggle to capture the deeper, network-level anomalies that emerge from the interactions between entities. To address this complexity, we propose a hybrid approach that integrates network-based measures with machine learning techniques for anomaly detection in waste transport data. Our method leverages advanced graph analysis techniques, such as sub-graph detection, community structure analysis, and centrality measures, to extract meaningful features that describe the network’s topology. We also introduce novel metrics for edge weight disparities. Further, advanced machine learning techniques, including clustering, neural network, density-based, and ensemble methods are applied to these structural features to enhance and refine the identification of anomalous behaviors.