Integer Ambiguity Resolution (IAR) concerns the successful resolution of unknown integer ambiguities present in carrier-phase measurements, which exhibit millimeter-level precision. In the context of Global Navigation Satellite System (GNSS) precise positioning and navigation, the IAR process is fundamental, as once ambiguities are correctly resolved, the phase data acts as ultra-precise pseudo-range data, thus enabling centimeter-level user positioning in real time. The Machine Learning for Ambiguity Resolution (ML4AR) project focuses on adopting innovative Artificial Intelligence (AI) solutions to enhance ambiguity resolution, a task currently addressed by non-AI methods, such as the Least-squares AMBiguity Decorrelation Adjustment (LAMBDA) method. Therefore, the ML4AR project delves into AI domains, exploring possibilities from Deep Learning techniques to Heuristic Optimization, ultimately aiming to enhance state-of-the-art performance for real-time Precise Point Positioning with Ambiguity Resolution (PPP-AR) users.