The explosive arrival of high-dimensional data in a wide variety of fields, including bioinformatics, finance, and image processing, offers serious problems to classical deep learning models, particularly when the feature distributions are not homogeneous. In this paper, a new multi-level ensemble-based adaptive deep learning strategy is proposed for effectively processing high-dimensional data with heterogeneous feature distributions. The reason the proposed model works is that it incorporates feature space partitioning ideas, adaptive deep learning models, and ensemble aggregation to enhance robustness, improve interpretability, and improve predictive performance. The model uses feature heterogeneity to segregate the input space, thus utilizing different deep learning models with different levels of subspace properties. This is followed by a dynamic aggregation mechanism of the ensemble that adapts to changing data distributions to maintain high accuracy and generalizability. Experiments on benchmark data, comprising gene expression data and remote sensing data, confirm that the proposed method is significantly more accurate, computationally and memory efficient and resistant to overfitting compared to the baseline models. The work presents a scalable framework to address the challenges brought about by high-dimensional, heterogeneous data, which is a manifestation of future, more reliable and flexible AI systems being brought to practical use.