Authorship attribution, a critical task in computational etymology, includes distinguishing the author of a text based on complex subtleties. In order to address the issues with authorship attribution in Bengali literary texts, this paper compares two deep learning models: Bidirectional Long Short-Term Memory (Bi-LSTM), enhanced by Adam and RMSprop optimizers, and Long Short-Term Memory (LSTM). Utilizing a dataset comprising writings from nine prominent Bengali authors, we explored the adequacy of these models in distinguishing between different composing styles. Thorough preprocessing, feature engineering, and an experimental setup were all part of our methodology to assess the LSTM and Bi-LSTM models' performance in terms of various metrics like accuracy, precision, recall, and F1 score. The results show that the Bi-LSTM model, optimized with Adam, achieved the highest testing accuracy of 92%, illustrating its predominant capability to generalize from preparing information to inconspicuous information. Also, we watched striking contrasts in model performance between training and testing phases, proposing potential zones for enhancement in demonstration preparation and generalization. This study not only underscores the potential of LSTM and Bi-LSTM models in authorship attribution but also highlights the significance of optimizing model configurations to improve performance. Further research aims to improve these models, look into additional include sets, and expand the analysis to other languages and literary forms.