The integration of advanced machine learning techniques with traditional optimization methods has emerged as a dominant theme across various research areas, significantly enhancing the adaptability, efficiency, and accuracy of solutions in dynamic and real-time scenarios. In wireless network optimization and design, deep unfolding frameworks and graph neural networks are revolutionizing joint optimization problems, while fast adaptation algorithms are ensuring quick responses to network changes. Communication networking and UAV-assisted systems are benefiting from novel decoupling techniques and bounds, along with innovative approaches for interference mitigation and secure communication. Graph-based machine learning and optimization techniques are advancing distributed systems and combinatorial problems through enhanced graph neural network architectures and energy-efficient hybrid spiking neural networks. Power systems and renewable energy integration are seeing sophisticated control mechanisms and advanced optimization models for grid stability and DER deployment. Spiking Neural Networks are making strides in energy-efficient deep learning with innovations in neuromorphic architectures and 3D hardware designs. Overall, the convergence of machine learning, optimization, and specialized techniques is driving significant progress across these fields, promising more efficient, secure, and scalable solutions for complex, real-world problems.