Depression is increasingly prevalent, leading to higher suicide risk. Depression detection and sentimental analysis of text inputs incross-domain frameworksare challenging. Solo deep learning (SDL) and ensemble deep learning (EDL) models are not robust enough. Recently,attention mechanismshave been introduced in SDL. We hypothesize thatattention-enabledEDL (aeEDL) architectures are superior compared toattention-not-enabledSDL (aneSDL) oraeSDL models. We designed EDL-based architectures withattention blocksto buildelevenkinds of SDL model andfivekinds of EDL model onfourdomain-specific datasets. We scientifically validated our models by comparing “seen” and “unseen” paradigms (SUP). We benchmarked our results against the SemEval (2016) sentimental dataset and established reliability tests. The mean increase in accuracy for EDL over their corresponding SDL components was 4.49%. Regarding the effect of attention block, the increase in the mean accuracy (AUC) ofaeSDL overaneSDL was 2.58% (1.73%), and the increase in the mean accuracy (AUC) ofaeEDL overaneEDL was 2.76% (2.80%). When comparing EDL vs. SDL for non-attention and attention, the meananeEDL was greater thananeSDL by 4.82% (3.71%), and the meanaeEDL was greater thanaeSDL by 5.06% (4.81%). For the benchmarking dataset (SemEval), the best-performingaeEDL model (ALBERT+BERT-BiLSTM) was superior to the bestaeSDL (BERT-BiLSTM) model by 3.86%. Our scientific validation and robust design showed a difference of only 2.7% in SUP, thereby meeting the regulatory constraints. We validated all our hypotheses and further demonstrated thataeEDLis a very effective and generalized method for detecting symptoms of depression in cross-domain settings.