The rapid advances in artificial intelligence (AI) and high-performance computing (HPC) are transforming the landscape of engineering design, and the concentrated solar power (CSP) tower sector is no exception. As these technologies increasingly penetrate the energy domain, they bring new capabilities for addressing the complex, multi-variable nature of receiver design and optimisation. This review explores the application of AI-driven generative design techniques in the context of CSP tower receivers, with a particular focus on the use of metaheuristic algorithms and machine learning models. A structured classification is presented, highlighting the most commonly employed methods, such as Genetic Algorithms (GAs), Particle Swarm Optimisation (PSO), and Artificial Neural Networks (ANNs), and mapping them to specific receiver types: cavity, external, and volumetric. GAs are found to dominate multi-objective optimisation tasks, especially those involving trade-offs between thermal efficiency and heat flux uniformity, while ANNs offer strong potential as surrogate models for accelerating design iterations. The review also identifies existing gaps in the literature and outlines future opportunities, including the integration of high-fidelity simulations and experimental validation into AI design workflows. These insights demonstrate the growing relevance and impact of AI in advancing the next generation of high-performance CSP receiver systems.