The rapid growth of digital collections has intensified the need for accurate and efficient book classification in digital libraries, yet manual cataloging remains labor-intensive and resource-demanding. Although deep learning approaches achieve strong performance in text classification, their high computational cost and limited interpretability hinder adoption in real-world library environments, particularly in small and medium-sized libraries with constrained resources. This study explores the feasibility of lightweight machine learning (ML) models as practical and resource-efficient methods for automated book genre classification. A curated subset of the Kaggle Books dataset was preprocessed through data cleaning, normalization, and text vectorization, yielding 56,260 records across multiple categories. A set of ML models was evaluated for their effectiveness in automated genre classification. Experimental results show that Logistic Regression outperformed other models, followed by Ridge, LinearSVC, Multinomial Naïve Bayes, and K-Nearest Neighbors, whereas tree-based models demonstrated relatively lower effectiveness and higher computational costs. These findings validate the applicability of linear and probabilistic models for bibliographic categorization, offering a practical entry point for libraries that have not yet explored automation. This research bridges the gap between traditional cataloging and AI-driven knowledge organization by demonstrating that lightweight ML models can serve as effective decision-support tools, particularly for resource-constrained libraries. While full automation remains challenging due to the stringent demands of accuracy and interpretability, incremental adoption of interpretable, resource-efficient models offers a realistic pathway toward Human-in-the-Loop paradigms, mitigating misclassification risks while advancing digital libraries toward more adaptive and intelligent services.