Pneumonia is a predominant cause of illness and death globally, particularly affecting vulnerable groups such as children, the elderly, and immunocompromised individuals. Timely and precise diagnosis is essential for effective therapy; however, conventional chest X-ray (CXR) evaluation by radiologists is prone to human error and constrained availability, especially in resource-limited environments. Recent breakthroughs in deep learning have facilitated automated and highly precise medical picture analysis, presenting a possible alternative for pneumonia identification. About 473,780 cases of pneumonia were reported in India in 2022–2023 and throughout this time, pneumonia was caused by 11,497 baby fatalities that aged between 1 to 12 months and 4,571 pediatric pneumonia-related deaths have been reported that aged between 1 to 5 years. In 2024 the annual incidence rate of community acquired Pneumonia in India is estimated to be between 5 and 11 per 1,000 people. This research proposes a system named PneumoSense, an intelligent automatic pneumonia diagnosis system employing DenseNet121, a deep learning model recognized for its efficacy in medical imaging applications. PneumoSense is an automated, intuitive interface that allows users to upload an x-ray image for analysis to ascertain the presence of pneumonia. Upon detection, the algorithm generates a prediction score and advises medical consultation. The model underwent comprehensive testing against various lung illnesses, such as COVID-19 pneumonia, fibrosis, and effusion, confirming its reliability in practical applications. Experimental findings indicate that DenseNet121 surpasses other deep learning algorithms, including CNN, ResNet50, and NASNet, attaining superior recall of 0.9795 and AUC score of 0.9808. The workflow of PneumoSense is also discussed in the paper. PneumoSense diminishes diagnostic inaccuracies, improves accessibility, and provides a feasible AI-driven substitute for manual diagnosis. This study underscores the revolutionary capacity of deep learning in medical diagnostics, facilitating early pneumonia detection, enhancing patient outcomes, and alleviating the workload on radiologists.