The immune system presents some of the most complex challenges in biology, encompassing nonlinear interactions, high-dimensional regulatory mechanisms, and substantial variability across individuals and contexts. As a result, traditional model-driven approaches often fall short in optimizing experimental conditions or therapeutic strategies. Black-box optimization methods—particularly Bayesian optimization (BO) and evolutionary algorithms (EAs)—offer powerful tools for guiding biological discovery when mechanistic understanding is incomplete or intractable. These algorithms iteratively propose informative experiments by learning from noisy, expensive, and sparse data, enabling efficient exploration of vast experimental spaces. In this review, we provide a comprehensive overview of black-box optimization methodologies and their applications in life science, with a particular focus on immunology and allergy research. We detail how black-box optimization is transforming various stages of biomedical R&D, from molecular design (e.g., antibodies, peptides) and gene circuit tuning to culture protocol optimization and patient-specific dose adjustment. We highlight key algorithmic advances, including constrained, multi-objective, parallel and high-dimensional BO, as well as recent developments such as grey-box optimization and transfer learning. Practical considerations, such as software tools and reproducibility-enhancing checklists, are also discussed. By integrating black-box optimization with automated experimentation platforms and high-throughput biological systems, researchers can accelerate discovery, personalize interventions, and systematically optimize complex immunological processes. We argue that black-box optimization will become a foundational component of experimental design and decision-making in the life sciences, bridging computational strategies with biological insight in increasingly adaptive and interpretable ways.
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