A quote, sometimes known as a quotation, is a well-known phrase or term that serves several objectives, in-cluding emphasis, elaboration, and comedy. Most of the time, the right use of quotations can make a statement more formal and convincing. However, the quantity of quotes an individual is familiar with or recalls often limits their ability to use them effectively. Given the quote's unique qualities, this work explores the automated categorization of Bengali quotes into 27 separate categories using machine learning (ML) and deep learning (DL). A bespoke dataset of 1464 quotes was carefully gathered from multiple online platforms. Next, we conducted extensive data preprocessing, which included text normalization, tokenization, stopword removal, lemmatization, and the use of RandomOverSampler to resolve class imbalances by oversam-piing minority classes. Then we investigated four models: Logistic Regression (LR), Multinomial Naive Bayes (MNB), Long Short-Term Memory (LSTM), and Bidirectional LSTM(Bi-LSTM). The experimental results demonstrated that the Bi-LSTM model obtained the highest accuracy of 94.63%, surpassing the LSTM model's 92.84%, LR's 67.06%, and MNB's 62.65% accuracy, demonstrating the effectiveness of contextual embeddings in Bengali quote categorization tasks.