Leukemia, a cancer hampering the bone marrow and blood, urges early detection for an improved prognosis. This study presents an innovative approach to leukemia prediction utilizing an ensemble of Deep Belief Networks (DBN) and Artificial Neural Networks (ANN). The primary aim is to build robust predictive models for early leukemia diagnosis using the Leukemia_GSE gene expression dataset. ANN and DBN applications demonstrate remarkable capability in managing complex datasets by allowing the extraction of hierarchical data representations and the integration of gene expression patterns. By combining these approaches into an ensemble framework, the study aims to enhance prediction accuracy while mitigating the limitations inherent in each approach. This ensemble strategy not only boosts the robustness and reliability of leukemia predictions but also underscores the potential of ensemble frameworks in medical research. It provides crucial insights into the strategic application of machine learning techniques to significantly advance disease detection and prediction. Accentuating the critical importance of pre-mature leukemia detection, the study reports an accuracy of 96.1%, surmounting contemporary methods such as K-Nearest Neighbor (KNN), Naive Bayes (NB), Logistic Regression (LR), and Support Vector Machines (SVMs). The findings demonstrate that the hybrid ensemble framework offers superior predictive accuracy.