In recent years, the evaluation of corporate sustainability performance has become one of the primary concerns of investors, regulatory bodies, and capital markets. The growing emphasis on Environmental, Social, and Governance (ESG) criteria in investment decisions has increased the need for the development of accurate and intelligent models for assessing corporate sustainability. However, most traditional sustainability scoring models are primarily based on the analysis of isolated indicators and structured data, and they are often incapable of capturing the complex relationships among companies, industries, and stakeholders. This limitation reduces assessment accuracy and weakens the ability to predict future sustainability performance.The aim of this study is to develop an intelligent sustainability scoring model for publicly listed companies using Graph Neural Networks (GNNs). In the proposed framework, companies are represented as nodes in a graph, while financial, industrial, ownership, and informational relationships among them are modeled as edges. By employing graph deep learning algorithms, hidden patterns and structural dependencies among companies are extracted and incorporated into the sustainability scoring process.This research adopts an applied and developmental approach within a quantitative research framework. The required data include financial indicators, ESG metrics, ownership information, and inter-company relationship data collected from listed companies. After data preprocessing, the Graph Neural Network model is developed and compared with conventional machine learning techniques, including Random Forest, XGBoost, and Multilayer Perceptron (MLP) neural networks. Model performance is evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Accuracy, and the Coefficient of Determination (R²).The findings indicate that incorporating graph structures and inter-company relationships can significantly improve the accuracy and explanatory power of sustainability scoring models. Furthermore, the proposed model provides an effective decision-support tool for investors, corporate managers, and capital market policymakers in promoting sustainability-oriented decision-making and sustainable development.