Mental health is an integral part of human welfare in the present era. The present research aims to bring attention to mental health issues with the help of social network, which turns into an influential forum for depressed individuals to express their sentiments and emotions. In this work, a contemporary depression detection framework that incorporates a prominent feature extraction approach and an optimized Gated Broad Learning System (G-BLS) is proposed. The feature extraction method captures Syntactic, Semantic, and Sentiment information of words and forms 3S feature embeddings. The extracted 3S feature vectors are then fed into the G-BLS model for depressive tweet classification. The G-BLS network often transfers redundant hidden nodes to the output layer because of its structural properties. To address this problem, a new binary optimization algorithm abbreviated as T-pHBGO is introduced by hybridizing the Honey Badger (HB) algorithm and Gannet Optimizer (GO) along with a T-shaped binary transfer function and population reduction strategy. The T-pHBGO algorithm selects the relevant and ideal neuron nodes to intensify the classification performance. The extensive experiments are conducted using two distinct Twitter corpora from the Kaggle repository. Accuracy and Root Mean Square Error (RMSE) measures are used to evaluate the system in contrast to other existing depression detection models. The comparative outcomes revealed that the composition of 3S word embedding, G-BLS model, and T-pHBGO algorithm surpasses the competing methods with an accuracy of 99.2% and 96.3%, RMSE rate of 0.0592 and 0.0417 for Sentiment 140 and Twitter depression corpus. The Analysis of Variance F-test confirmed the statistical significance of the results. The cross-validation results demonstrate that the proposed method is capable of maintaining its performance despite the use of different test data, as evidenced by the average classification accuracy.