<p>Electroencephalogram signals are used to depict emotional and stress disorders. To overcome issues of existing models, novel transfer learning-based bioinspired ensemble model for preemptive detection of stress and emotional disorders is discussed. The proposed model includes features of mel-frequency cepstral coefficient, iVector, cosine, Fourier and wavelet components. A combination of these features is processed via gray wolf optimization which aims at variance maximization across. The selected features are converted into 2D representation and processed via a transfer learning-based convolutional neural network model combining ResNet 101, MobileVNet, and YoLo models. The classified results from these models are further cross-validated via use of ensemble classification that combines Naïve Bayes, support vector machine, random forest, logistic regression, and multilayer perceptron models. These classifiers perform several post-processing tasks involving identification of disease spread probability, estimation of future diseases, etc. The proposed model was trained on DEAP and interface datasets, compared w.r.t. various state-of-the-art methods, in relation to accuracy, recall, precision, area under the curve, and delay performance. Based on this performance, proposed model’s effectiveness was noticed, showcasing 8.5% higher accuracy, 8.3% higher precision, 5.9% better recall, 4.5% better AUC, and 14.9% faster classification performance, which makes it highly useful for clinical deployments.</p>