Topic modeling is a natural language processing technique used to discover latent themes and abstract topics from text corpora by grouping co-occurring keywords. Although widely researched in English, topic modeling remains understudied in Bengali due to a lack of adequate resources and initiatives. Existing Bengali topic modeling research lacks exploration of modern methodological approaches, reproducible implementations, diverse datasets, and systematic benchmarking. To bridge these gaps, this study presents Graph-based Hybrid Topic Model (GHTM), a novel architecture that synthesizes the distinct strengths of multiple paradigms by unifying statistical (TF-IDF), semantic (GloVe), and structural (GCN) modalities to construct semantically aligned document representations. GHTM later employs matrix factorization for topic extraction, offering a computationally lightweight alternative to resource-intensive neural and graph-based approaches. Through comprehensive benchmarking across three Bengali datasets of varying scales, GHTM consistently surpasses both traditional and contemporary topic modeling approaches, achieving improved topic coherence (NPMI: 0.27-0.28) and high topic diversity while maintaining computational efficiency. The model also outperformed established graph-based models on the English 20Newsgroups benchmark. Additionally, we introduce NCTBText, a diverse Bengali textbook-based dataset comprising 8,650 text documents, providing much-needed topical diversity beyond newspaper-centric Bengali corpora.

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