The process of decision-making in football is characterized by a complex interplay between spatial positioning, opponent pressure, and player intent. In this research, we introduce xReceiver, a real-time Graph Neural Network (GNN) framework designed to predict the optimal passing target by modeling on-field interactions as dynamic graphs. Each player is represented as a node with positional and contextual features, while potential passing lines form weighted edges characterized by distance, angle, and pressure metrics. We have developed a Message-Passing Neural Network (MPNN) that is trained using a combination of tracking data and event data from professional matches. Our model achieves 65.22% accuracy in identifying the actual chosen receiver and 95.65% accuracy within its top three suggestions. xReceiver further offers quantification of each option's likelihood, threat, and creativity, enabling performance analysts to evaluate over 1,000 passes in seconds.