Accurate prediction of Drug–Target Interactions (DTIs) plays a central role in accelerating drug discovery and improving therapeutic interventions. In this paper, we introduce a novel deep learning framework that integrates specialized Convolutional Neural Networks (CNNs) and Fully Connected Neural Networks (FCNNs) to extract and learn complex molecular features from both SMILES representations and protein amino acid sequences. Unlike conventional methods that rely primarily on one-hot encoding, our approach leverages dense, learned embeddings to capture nuanced structural and physicochemical relationships. We benchmark our model on widely used datasets, including BindingDB and DAVIS, both of which focus on drug–target binding affinity. The results demonstrate high specificity and accuracy across diverse data splits, albeit with some variation in sensitivity and precision depending on the dataset and encoding strategy. These findings suggest that while embedding-based representations offer significant potential for capturing subtle interaction patterns, carefully tuning hyperparameters and considering dataset characteristics remain pivotal for optimal performance. Altogether, our study highlights the promise of advanced deep learning techniques in streamlining virtual screening, guiding in vitro validation, and ultimately expediting the drug design pipeline.