Continuous casting of aluminum (Al) deoxidized steels demands careful inspection due to the occurrence of submerged entry nozzle (SEN) clogging, leading to unexpected production stops. Recognizing the castability of a specific “cast” by monitoring the condition of the SEN is essential for uninterrupted casting. With this information prior to casting, operators can take preventive action against possible clogging occurrences, thus reducing unplanned downtimes. In response to the severe implications of SEN clogging, this work introduces a novel way to forecast castability of Al‐killed steels. A hybrid model is proposed that integrates the adaptive neuro‐fuzzy inference system (ANFIS) and long short‐term memory (LSTM) networks. The output of the model helps to anticipate the event of clogging by analyzing both the past condition of the SEN and changes in the steel chemistry during the transport of the steel ladle from refining to the casting process. A comprehensive analysis of 150 casts helped to build the ANFIS algorithm for estimating the castability index (CI) parameter from steel chemistry. LSTM algorithm is used as a subsequent step to forecast castability in the next 20–25 min. Discrepancies between the predictive response and the actual conditions are reported. Although the real‐time implementation of the proposed model is the ultimate goal, the focus of this work was to present the methodology and demonstrate its potential.