To avoid the rising number of car crash deaths, which are mostly caused by drivers' inattentiveness, a paradigm shift is expected. The knowledge of a driver's look area may provide useful details about his or her point of attention. Cars with accurate and low-cost gaze classification systems can increase driver safety. When drivers shift their eyes without turning their heads to look at objects, the margin of error in gaze detection increases. For new consumer electronic applications such as driver tracking systems and novel user interfaces, accurate and effective eye gaze prediction is critical. Such systems must be able to run efficiently in difficult, unconstrained conditions while using reduced power and expense. A deep learning-based gaze estimation technique has been considered to solve this issue, with an emphasis on WSN based Convolutional Neural Networks (CNN) based system. The proposed study proposes the following architecture, which is focused on data science: The first is a novel neural network model that is programmed to manipulate any possible visual feature, such as the states of both eyes and head location, as well as many augmentations; the second is a data fusion approach that incorporates several gaze datasets. However, due to different factors such as environment light shifts, reflections on glasses surface, and motion and optical blurring of the captured eye signal, the accuracy of detecting and classifying the pupil centre and corneal reflection centre depends on a car environment. This work also includes pre-trained models, network structures, and datasets for designing and developing CNN-based deep learning models for Eye-Gaze Tracking and Classification.