Mutagenicity is concerning due to its link to genetic mutations, which can lead to cancer and other adverse effects. Early identification of mutagenic compounds in drug development is crucial to prevent unsafe candidates and reduce costs. While computational techniques, especially machine learning (ML) models, have become prevalent for mutagenicity prediction, they typically rely on a single modality. Our work introduces a novel stacked ensemble mutagenicity prediction model that integrates multiple modalities, including SMILES and molecular graphs. These modalities capture diverse molecular information such as substructural, physicochemical, geometrical, and topological features. We use SMILES for deriving substructural, geometrical, and physicochemical data, while a graph attention network (GAT) extracts topological information from molecular graphs. Our model employs a stacked ensemble of ML classifiers and SHAP (Shapley Additive Explanations) to identify the significance of classifiers and key features. Our method outperforms state-of-the-art techniques on two standard datasets, achieving an area under the curve of 95.21% on the Hansen benchmark dataset. This research is expected to interest clinicians and computational biologists in translational research.