In this paper, we introduce CrimeGNN, a novel application of Graph Neural Networks (GNNs) specifically designed to uncover hidden communities within criminal networks. As criminal activities increasingly rely on complex network structures, traditional methods of network analysis often fall short in detecting the intricate and dynamic communities within these networks. Leveraging the power of GNNs, CrimeGNN provides an advanced and specialized solution to this problem. The model ingests a graph structure of a criminal network, where vertices represent individuals and edges represent relationships between them. CrimeGNN aims to identify a partition of the vertex set, such that each subset represents a distinct community within the network, maximizing the modularity function. Experimental results on several benchmark datasets demonstrate the effectiveness of CrimeGNN, outperforming existing methods in terms of both accuracy and computational efficiency. The proposed framework offers significant potential for aiding law enforcement agencies in proactive policing and crime prevention measures by providing a more in-depth understanding of the structure and operation of criminal networks.