<p>Electrical Impedance Tomography (EIT) as a non-invasive imaging method, faces two main challenges due to the inherent difficulty in solving the inverse image reconstruction problem. The main difficulty lies in simultaneously achieving two key goals: Accurately estimating conductivity values and clearly reproducing structural boundaries. Existing methods typically excel at one aspect while compromising the other. In this work, we present an advanced ensemble learning approach combining complementary deep learning models to overcome these challenges. The method integrates two specialized ensembles: A gradient-boosting framework with an enhanced 1D-CNN-GRU for conductivity prediction and a Dense Attention Network (DA-Net) for boundary shape reconstruction, unified through a stacking ensemble with a custom loss function optimizing both conductivity accuracy and structural preservation by combining Rooted Mean Squared Error (RMSE) for quantitative accuracy and Image Correlation Coefficient (ICC) in a weighted formulation. The model is first validated in a well-controlled water tank setting. Simulation results show an ICC of 0.975 and Relative Image Error (RIE) of 0.171. Experimental validation further confirms the method’s robustness, maintaining consistent reconstruction quality for different object positions and materials. The method was then evaluated using a more complex, simulated lung model that includes realistic anatomical features, changes in conductivity distribution, and tissue anomalies. Results demonstrate improved performance compared to baseline methods by an average 2.67% increase in ICC and 24.55% reduction in RIE. The results confirm that the novel systematic ensemble approach successfully addresses the dual challenges of conductivity prediction and boundary preservation for effective anomaly detection in EIT imaging.</p>