Electrical Impedance Tomography (EIT) offers a versatile imaging modality with a multitude of applications, although it encounters accuracy limitations. In this study, we present a novel systematic framework that integrates a neural network (NN), active learning, and transfer learning to optimize electrode placement, improving image reconstruction performance based on user‐defined metrics. Given the many‐to‐one mapping between electrode configuration and the performance metric, our approach utilizes a NN that predicts performance metrics from electrode placement input. To maintain NN’s prediction accuracy for unseen electrode configurations, we maximize performance metrics while iteratively updating the neural network via active learning during the optimization process. We employ transfer learning to expedite optimization of electrode placements for time‐consuming iterative reconstruction techniques by fine‐tuning a NN initially trained on one‐step reconstruction data. We validate our method using two representative reconstruction methods: one‐step reconstruction with Newton’s one‐step error reconstructor (NOSER) prior and the iterative Total Variation (TV) method. This research underscores the potential of our proposed framework in addressing EIT’s inherent limitations and augmenting its performance across diverse applications and reconstruction methods. The framework could potentially contribute to the advancement of non‐invasive medical imaging, structural health monitoring, strain sensing, robotics, and other fields that depend on EIT.This article is protected by copyright. All rights reserved.