Our research explores the integration of Artificial Intelligence (AI) within mirrorless cameras to automate color grading and editing processes, utilizing Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). The project aims to harness AI's potential to analyze and learn from a photographer's historical stylistic choices, enabling personalized image enhancement directly in-camera. Current DSLR and mirrorless cameras already leverage AI for various functions such as autofocus, noise reduction, and image stabilization. Building on these applications, my project seeks to add a layer of creative automation by training models on a photographer's edited images, thereby recognizing and applying their unique stylistic traits to new photos. Our approach aims to streamline the editing workflow, offer inspiration, and maintain artistic individuality, addressing concerns over the potential homogenization of creative expression in photography. By embedding this technology directly into camera hardware, it provides an innovative tool for photographers to instantly visualize and apply their preferred aesthetics, challenging the traditional post-processing workflow. Our research not only advances the technical capabilities of camera systems but also explores the balance between technological innovation and the preservation of artistic integrity in this age of photography.