This study extends previous work on systematic review of the literature on the impact of climate change on cultural heritage. Our objective is to develop a robust classifier capable of categorizing new research publications into distinct thematic topics. We began by using six different topic modeling techniques—Latent Dirichlet Allocation (LDA), Latent Semantic Indexing (LSI), Hierarchical Dirichlet Process (HDP), Non-Negative Matrix Factorization (NMF), Structural Topic Modeling (STM), and Correlated Topic Modeling (CTM)—to generate topic probability distributions for each document. These topic probabilities served as input features for various classifiers. Additionally, we incorporated BERT embeddings to capture nuanced semantic information from abstracts and leverage large language models for improved topic representation. To further enhance performance, we developed ensemble methods that integrate multiple classifiers, achieving higher classification accuracy. Finally, we applied the optimized model to classify an additional dataset of 259 new papers, enabling a refined categorization of climate change’s impact on cultural heritage. We extended our work by applying the same topic model techniques to a new theme: over-tourism, to further investigate possible threats to cultural heritage.