Fog computing is a key component of the internet, performing vital work in providing real-time and low-latency services by bringing computation and storage capabilities closer to the edge of the data source. Being a decentralised and heterogeneous resource-constrained environment, it incurs multiple security implications and becomes a target of advanced intruder attempts. Its core challenge is to develop robust yet effective fog networks, considering the changing attributes of network topologies, the limitations of fog nodes in computation, and various types of attack vectors. Even the type of IDS, which is quite suitable for the centralised setting of cloud operations, scarcely maintains an excellent balance of high detection accuracy, low false positives, and the ability to respond in real-time once incorporated into the fog computing setting. The vast majority of the features of random forest classifiers, as determined by the EALC model, are among the future proposals for an evolving multilevel artificial intelligence-based intrusion detector that would further enhance the area of fog computing infrastructure against such monumental challenges. This designed mixture of multiple pathways works better in terms of feature discrimination, reduction of false notifications, and rapid detection speed, fulfilling real-time specifications that necessitate passenger and load requirements in fog situations. There has been extensive experimental testing of the EALC-based IDS using half a dozen performance indicators. The structural conditions of the state-of-the-art in intrusion detection have indicated promising improvements over the personal key institutional features, including good accuracy, precision, recall, F1 score, and low false positive readings, with minimal detection lags.