In situations where there is a shortage of ambulances, hospitals, physicians, and other medical personnel, it is crucial to closely monitor and track the health of an affected individual in a limited capacity. This is because the health of the affected role may immediately deteriorate, potentially leading to loss of life. Fog computing exhibits reduced latency compared to cloud computing due to the utilization of compact, autonomous devices. Healthcare enterprises are allocating resources to fog computing, facilitated by the Internet of Things, in order to enhance public services and perhaps save billions of lives. This state-of-the-art fog computing platform has the potential to enhance the accuracy and efficiency of fog computing systems. Among women, breast cancer is the most prevalent form of cancer. Utilizing computerized diagnostics can significantly enhance a patient’s chances of survival. This operation is performed by certified medical personnel. The medical profession is delicate, hence artificial intelligence is essential. This study develops a series of cascaded convolutional neural networks and optimizes their hyper parameters using spider monkey optimization (SMO). Patients can also receive notifications to adhere to their medication regimen or maintain a healthy diet. Multiple researchers and medical institutes are storing the extensive dataset in the cloud for future utilization. The trials demonstrate that the deliberate exertion enhances stability beyond the current highest level of achievement.