Messenger RNA (mRNA) subcellular localization is crucial for controlling gene expression and organism development. Accurate prediction of mRNA localization is essential for understanding gene functions and regulatory mechanisms. Despite progress in current models, limitations in feature representation have created a significant gap in prediction accuracy. To bridge this, we proposed the HyEnLoc, a novel method that integrates deep learning with an ensemble of specialized submodels to refine mRNA localization predictions. HyEnLoc consists of three submodels: CompAnalyz analyzed nucleotide composition, PhysioFeats focused on nucleotide physicochemical properties, and NeuroEncode leveraged a sophisticated deep learning framework. These are integrated using the Boosting algorithm to enhance predictive efficacy, while the Random Forest algorithm assisted in feature selection by removing redundant vectors, and the Synthetic Minority Over-sampling Technique addresses data imbalances. The unique combination of multiple feature extractors and deep learning in HyEnLoc has been validated through experimental evaluations, underscoring its superiority in accuracy and robustness over advanced competitors. In comparative tests, HyEnLoc outperformed existing predictors with an accuracy of 86.73%, demonstrating its powerful predictive capabilities. The source code for HyEnLoc is available at https://anonymous.4open.science/r/HyEnLoc-8BAF .