<p>The growing demand for high-quality film and television adaptations of literary works necessitates predictive tools capable of estimating the potential effectiveness of such adaptations. By examining the linguistic content of literary scripts, this study suggests a deep learning (DL)-based model for forecasting adaption success. By examining the linguistic content of literary scripts, a novel Artificial Fish Swarm-tuned Attention-refined Bidirectional Long Short-Term Memory (AFS-Att-BiLSTM) network model is suggested for predicting adaption success. The model is trained and validated on a literary adaptation success dataset of over 5000 literary works and their corresponding adaptations, covering multiple genres and release platforms. Preprocessing steps include text cleaning, tokenization, and normalization of numerical performance indicators. Key predictive features include narrative coherence, sentiment trajectories, emotional intensity, character interactions, genre classification, and historical adaptation metrics such as audience ratings, box office collections, and critic reviews. The AFS was used for optimizing the model’s parameters to enhance prediction accuracy, and refined BiLSTM was used to capture contextual and sequential dependencies in literary scripts. An attention mechanism highlights narrative elements most strongly correlated with successful adaptations, offering interpretable insights for producers. The suggested model was implemented using Python. The findings show that the AFS-Att-BiLSTM approach performs better than baseline methods, achieving superior results, with accuracy ranging from 93–95% to forecast adaptation outcomes, and significant variance in audience reception and commercial success. These findings underscore the potential of DL to transform literary scripts into data-driven predictors of adaptation effectiveness, thereby supporting informed decision-making in greenlighting, promotional strategies, and creative optimization.</p>