The increasing number of objects in orbit makes accurate modelling essential to predict the evolution of the space environment. Although Space Traffic Management (STM) supports orbital monitoring and tracking, artificial threats and natural phenomena, such as Space Weather (SW) events, can increase orbital perturbations and collision risks for critical space assets. Predicting these cross-domain effects is essential to accelerate decision-making and prevent losses. 

Addressing this challenge requires integrating data from multi-domain sensor networks to identify emerging threats and assess their potential impacts. AI/ML tools can support this process by enabling continuous monitoring and reliable risk assessment while reducing operator workload. However, ensuring the trustworthiness of these tools remains a key challenge.