Globally, pneumonia remains the leading cause of mortality among infants, warranting immense attention from medical professionals. Expert radiologists commonly resort to chest X-rays to identify pneumonia and other respiratory ailments. However, the intricacies of this diagnostic process often result in disagreements among radiologists regarding the final diagnosis. To effectively mitigate the detrimental impact of pneumonia on patients, an early and accurate diagnosis becomes imperative. In this context, computer-aided diagnostics have emerged as a promising avenue to enhance the precision of pneumonia detection. Recent investigations have revealed that leveraging deep learning models yields more accurate predictions compared to conventional approaches. As part of our current research endeavour, we introduce a novel model named DepneumoNet specifically designed for pneumonia detection utilizing chest X-ray images. Through rigorous experimentation and analysis, the proposed DepneumoNet architecture has demonstrated a commendable accuracy level of 86.2% in accurately identifying pneumonia cases. This signifies a significant advancement in the realm of pneumonia diagnosis and holds immense potential for improving patient outcomes by facilitating early intervention and treatment.