Categorical search and category-wise book recommendation are two common tasks for online booksellers. But for a machine to understand this category from a given text is still challenging work, where machine learning is a widely used tool at present. Though in the English language, with the availability of rich datasets and corpus, machine learning-based categorization and recommendation have reached a standard level, in the Bengali language, to reach the standard, still needs a long way to go. One key reason is the lack of availability of a rich Bengali dataset. The aim of this research was to make a dataset first for the book’s genre identification from its given summary and to explore which supervised classifier performed best on that dataset for classifying the genres. Before that, we performed several essential preprocessing steps essential to prepare our dataset fit for the algorithms. Six machine learning classifiers were applied to the dataset, and it was observed that Naive Bayes performed best with an accuracy of 68% followed by XGB with an accuracy of 67%.