Graph Neural Networks are one of the most robust paradigms for analyzing complex, related, and tightly coupled datasets. The main objective of this paper is to use any of the emerging data analytics models to develop a common recommendation system for consumer products used in daily human life. Thus, this paper implements the GNN model for interlinking various datasets of consumer products, like food, medicine, and others. The paper’s novelty is interconnecting multiple datasets, interlinking them using GNN-based graphs, and extracting features to provide accurate predictions. The applications of GNNs are explored to understand the functionalities and capability of handling several heterogeneous datasets to develop a unified recommendation system. The existing recommendation systems struggled to obtain the inter-relationships among multiple consumer datasets and interconnecting heterogeneous datasets. In contrast, GNN can address the issues and challenges. A graph structure is created dynamically to interconnect all the consumer product data items based on structural, contextual, usage, and user opinion and experience. A benchmark dataset was obtained from UCI and Kaggle repositories, and GNN in Python was experimented with to understand its efficacy. The experimental outputs demonstrate that the GNN model is highly efficient in interconnecting heterogeneous datasets and creating similarity-aware recommendation systems.