The optimisation of drilling fluids remains one of the most important parts of oil and gas well construction, since they have a direct impact on wellbore stability, rate of penetration, formation integrity, and overall drilling efficiency. Conventional modelling approaches have long been limited by their inability to accurately reflect the complex, non-linear interactions that regulate fluid behaviour in high-pressure, high-temperature (HPHT) and heterogeneous geological environments. In recent years, artificial intelligence (AI) has emerged as a disruptive paradigm, offering data-driven tools for predicting, monitoring, and optimising drilling fluid performance in real-time. Therefore, this chapter presents a comprehensive overview of AI applications in drilling fluid engineering. It focuses on real-time monitoring and prediction of critical parameters such as equivalent circulating density, rheology, and filtration loss, as well as property prediction through machine learning and deep learning models. In addition, it investigates drilling fluid formulation optimisation using nanomaterials and sophisticated additives, as well as anomaly detection for early kick and lost circulation occurrences. Case studies from industrial platforms, such as Schlumberger's Delfi, Halliburton's i-Energy, and Baker Hughes’ digital solutions, show the increasing usage of AI technology in the upstream business, with verifiable improvements in drilling performance and cost reduction. The research also examines the data challenges associated with AI deployment in drilling environments, including the quality, heterogeneity, and accessibility of sensor-derived downhole data, as well as the risks of overfitting, model interpretability, and interactions with legacy systems. Furthermore, the environmental and economic impacts of AI-driven drilling fluids are severely assessed. Evidence suggests that predictive analytics and automated optimisation can dramatically cut non-productive time, reduce waste output, and promote regulatory compliance, aligning drilling methods with global sustainability goals. Looking ahead, future themes include the integration of AI with the Internet of Things (IoT) and digital twin frameworks, hybrid physics-AI modelling for enhanced reliability, and the utilisation of edge computing to facilitate decision-making in remote field operations. The increased emphasis on explainable AI would be vital for preserving trust, openness, and human oversight in critical drilling decision-making processes. Finally, the study concludes that AI has the potential to not only propel drilling fluid engineering into a new era of efficiency, safety, and sustainability, but also to serve as a key technology in the oil and gas industry's larger transition to digitalization and carbon-conscious operations.