The recent advancements in the research area are significantly pushing the boundaries of network optimization, resource allocation, and AI-driven services in wireless and edge computing environments. A notable trend is the integration of generative AI models, such as diffusion models, into network optimization problems, which is shown to enhance both the efficiency and accuracy of solutions. This approach is particularly promising in addressing complex, dynamic network scenarios where traditional methods fall short. Additionally, there is a strong focus on developing novel algorithms that balance fairness and computational efficiency in wireless networks, leveraging explainable machine learning models to ensure equitable resource distribution. The field is also witnessing innovative frameworks for network slicing tailored to AI services, which aim to dynamically allocate resources based on varying QoS requirements. Furthermore, the importance of data protection and privacy in AI-native 6G systems is being emphasized, with research highlighting the need for robust privacy-by-design principles. Overall, these developments indicate a shift towards more intelligent, adaptive, and secure network architectures capable of supporting the demands of next-generation applications.