Debugging high-dimensional state spaces in cloud continuum environments poses significant challenges, particularly when investigating extreme conditions such as high latency, competing on resources, or configuration anomalies. This paper presents a novel supervised machine learning-based approach to efficiently assist the debugging process by steering toward potential fault states in an automated way. Leveraging typical blueprint primitives, such as load balancers and temporal data storage in the presented case studies, Multi-Layer Perceptron (MLP) and Dense Neural Networks (DNN) were trained to predict the distance to extreme situations. The trained model informs a traversal mechanism that explores the state space using this heuristic, minimizing the time and consumed resources required to detect actual faults. The first experiments conducted with two foundational blueprint primitives (buffers and multi-tier load balancers) demonstrate the promising effectiveness of the approach in locating potential fault states. By integrating this method into cloud-edge debugging tools, developers can enhance not only fault localization but reliability and performance as well, particularly for extreme timing conditions. Future work will explore a wider set of primitives, as well as adjacency matrix representations and convolutional techniques, to improve applicability, scalability and robustness of the presented solution.