Abstract: The development of smart sustainable cities increasingly relies on advanced communication infrastructures to support intelligent transportation systems, energy management, and data-intensive urban services. The rapid expansion of Internet of Everything networks has led to rising energy consumption, posing a critical challenge for sustainable urban development. This research was conducted to address the need for energy efficient, high-performance wireless communication solutions capable of supporting large-scale, innovative city applications. In particular, High Altitude Platform Systems have emerged as promising communication enablers due to their wide coverage and deployment flexibility; however, their effectiveness is constrained by the energy efficiency and performance of antenna systems. This study investigates the application of Machine Learning techniques for the optimisation of 3D antenna structures to enhance communication efficiency. The 3D intelligent Microstrip Patch Multiple Input Multiple Output antenna operating at 28 GHz was designed and optimised using a Machine Learning-driven framework. The antenna design process was carried out using 3D digital Computer Simulation Technology software, enabling precise electromagnetic modelling and performance evaluation. Machine Learning algorithms were employed to systematically adjust antenna parameters, allowing the identification of optimal design configurations beyond conventional trial-and-error methods. The performance of the optimised antenna was evaluated using Quality of Service parameters for power efficiency and last mile connectivity. Comparative analysis with non-optimised antenna designs demonstrated substantial performance gains. The results reveal an improvement of up to 31% in power efficiency, accompanied by enhanced connectivity performance. These findings indicate that Artificial Intelligence-driven antenna design is a practical approach to developing sustainable, energy-efficient communication infrastructure for future, innovative city environments.