The ongoing technological revolution is reshaping the way we live, work and communicate, with Artificial Intelligence (AI) emerging as one of the most disruptive and influential forces behind this evolution. Within this domain, Machine Learning (ML) enables systems to learn from data and improve performance without explicit programming. Among the most influential architectures in the field of ML, Convolutional Neural Networks (CNNs) have established themselves as the standard for processing spatially structured data such as images and videos. The growing complexity of AI models and the demand for real-time processing highlight the limitations of relying solely on centralized cloud infrastructures. Edge computing, in this context, allows data to be processed closer to its source, reducing latency, bandwidth usage, and energy consumption.