As 5th Generation (5G) and Beyond 5G (B5G) networks evolve, dynamic resource allocation and management is crucial for supporting the diversity of devices and the mixed data traffic types. Network slicing enables the logical segmentation of an infrastructure to meet specific Quality of Service (QoS) requirements posed by applications, but factors such as fluctuating traffic, user mobility, and cross-slice interference, pose challenges towards proactive resource allocation. Traditional methods struggle with these factors, leading to inefficiencies. Therefore, this paper explores the concept of an AI-driven network performance prediction and resource allocation framework using Temporal Graph Networks (TGNs). By integrating TGN with the NS-3 simulator, the work in the paper demonstrates an efficient approach to predict network throughput. The proposed solution advances spatiotemporal Artificial Intelligence (AI) techniques enabling more accurate prediction of network performance and adaptive resource optimization, supporting dynamic network slicing. Find more information here: https://ieeexplore.ieee.org/document/11193687