The integration of machine learning (ML) techniques with Raman spectroscopy has emerged as a promising strategy for advancing cancer diagnostics through label-free, high-resolution molecular analysis. This review aims to map and synthesize current research directions in this rapidly evolving field by conducting a structured review of existing review articles. Using a curated dataset of 70 reviews retrieved from Scopus and Web of Science, we applied Latent Dirichlet Allocation (LDA) topic modeling to uncover dominant thematic clusters across the literature. Our findings reveal five key research axes: (1) instrumentation and signal acquisition, (2) data preprocessing and spectral denoising, (3) classification models and algorithmic pipelines, (4) biomedical applications in oncology, and (5) emerging trends including deep learning and hybrid methods. This thematic structure highlights both the maturity and fragmentation of the current knowledge landscape. We also discuss the limitations of our approach, including database and article-type restrictions, and the use of LDA as a single modeling method. By identifying underexplored areas and recurring methodological challenges, this review contributes to a clearer understanding of the research gaps and future opportunities at the intersection of ML and Raman spectroscopy for cancer research.