The Internet of Medical Things (IoMT) has emerged as a transformative domain in healthcare, offering unprecedented opportunities for remote monitoring, smart diagnosis, and automated medical services. However, the rapid development and deployment of IoMT applications have inadvertently attracted the attention of attackers, leading to an increased risk of security breaches. Attackers will use malware to exploit IoMT systems to get unauthorized access to them. To address this challenge, the proposed work introduces a Multi-View Contrastive Graph Neural Network (MVC-GNN) for malware detection in IoMT environments. The proposed approach leverages graph-based deep learning and contrastive representation learning to effectively distinguish benign and malicious network traffic to enable timely and accurate detection of malware. This work uses CICIoMT 2024 dataset, a state-of-the-art publicly available dataset, for a comprehensive evaluation of the proposed approach. Experimental results highlight the high efficacy of the approach, with an accuracy of 99.69% and an F1-score of 99.61%, indicate that the proposed work is robust and generalizable for malware detection in IoMT framework.