Optimization plays a central role in a wide range of industrial applications, where complex mathematical models must be solved efficiently to extract meaningful information from data. From enhancing security in networked systems to reconstructing high-quality images from incomplete measurements, modern optimization techniques provide fundamental tools to tackle challenging problems across different domains. Advances in numerical methods, coupled with increasing computational power, made possible novel approaches capable of handling large-scale, high-dimensional problems with growing efficiency and robustness. A key application of optimization in industrial settings is the design of intelligent systems that can adapt to dynamic environments. In cybersecurity, machine learning-based Intrusion Detection Systems (IDS) must be continuously updated to detect new and evolving threats. This introduces non-trivial optimization challenges, including continual learning strategies that enable models to retain past knowledge while incorporating new information without catastrophic forgetting. Efficient training mechanisms must balance adaptability with computational constraints, ensuring that models remain both accurate and scalable in real-time applications. In the context of imaging and signal processing, optimization techniques are widely used to solve inverse problems, where the goal is to reconstruct an unknown quantity from indirect or noisy observations. Applications in medical imaging, remote sensing, and shape optimization for surgery rely on advanced numerical methods to obtain stable and high-fidelity reconstructions. Variational formulations, iterative algorithms, and regularization techniques are essential tools for improving image quality while preserving important structural information. The interplay between theoretical advancements in optimization and their practical implementation is crucial in pushing the boundaries of these technologies. This minisymposium brings together researchers working at the interface of numerical optimization, machine learning, and industrial applications. By promoting discussions on both methodological advances and real-world challenges, the session aims to highlight the latest developments and open problems in this rapidly evolving field.