Long-Term morphine treatment leads to tolerance which attenuates analgesic effect and hampers clinical utilization. Recent studies have sought to reveal the mechanism of opioid receptors and neuroinflammation by observing morphological changes of cells in the rat spinal cord. This work proposes a high-content screening (HCS) based computational method, HCS-Morph, for predicting neuroinflammation in morphine tolerance to facilitate the development of tolerance therapy using immunostaining images for astrocytes, microglia, and neurons in the spinal cord. HCS-Morph first extracts numerous HCS-based features of cellular phenotypes. Next, an inheritable bi-objective genetic algorithm is used to identify a minimal set of features by maximizing the prediction accuracy of neuroinflammation. Finally, a mathematic model using a support vector machine with the identified features is established to predict drug-Treated images to assess the effects of tolerance therapy. The dataset consists of 15 saline controls (1 μl/h), 15 morphine-Tolerant rats (15 μg/h), and 10 rats receiving a co-infusion of morphine (15 μg/h) and gabapentin (15 μg/h, Sigma). The three individual models of astrocytes, microglia, and neurons for predicting neuroinflammation yielded respective Jackknife test accuracies of 96.67%, 90.00%, and 86.67% on the 30 rats, and respective independent test accuracies of 100%, 90%, and 60% on the 10 coinfused rats. The experimental results suggest that neuroinflammation activity expresses more predominantly in astrocytes and microglia than in neuron cells. The set of features for predicting neuroinflammation from images of astrocytes comprises mean cell intensity, total cell area, and second-order geometric moment (relating to cell distribution), relevant to cell communication, cell extension, and cell migration, respectively. The present investigation provides the first evidence for the role of gabapentin in the attenuation of morphine tolerance from phenotypic changes of astrocytes and microglia. Based on neuroinflammation prediction, the proposed computer-Aided image diagnosis system can greatly facilitate thedevelopment of tolerance therapy with anti-inflammatory drugs.