This study uses speech information from the DementiaBank Pitt Corpus to investigate deep learning models for the early identification of dementia. Classifying patients as dementia-positive (AD+) or negative (AD-) is the goal of the models, which examine transcripts of verbal exchanges between patients and therapists. Several architectures were used, such as Transformer, CNN, CNN + BiLSTM, Stacked Deep Dense Neural Network (SDDNN), and Attention-based LSTM. Important measures like accuracy, precision, recall, F1 score, specificity, and AUC were used to assess each model. Pre-trained GloVe embedding models consistently outperformed randomly initialized embedding models; the greatest accuracy (94.00%) and AUC (0.9350) were achieved by the Attention-based LSTM model. The research results demonstrate how well hybrid deep learning models—particularly those that incorporate attention mechanisms—capture complex linguistic features, providing a scalable and effortless means of diagnosing dementia at an early stage.