Optimizing costly black-box functions within a constrained evaluation budget presents significant challenges in many real-world applications.
Surrogate Optimization (SO) is a common resolution; however, the complexity of surrogate models and the sampling core (e.g., acquisition functions) often introduces proprietary elements, leading to a lack of explainability and transparency. 
While existing literature has primarily focused on enhancing convergence to global optima, the practical interpretation of newly proposed strategies remains underexplored, particularly in batch evaluation settings. In this paper, we propose Inclusive Explainability Metrics for Surrogate Optimization (IEMSO), a comprehensive set of model-agnostic metrics designed to enhance the explainability of the SO approaches. Through these metrics, we provide both intermediate and post-hoc explanations, enabling practitioners to build trust before and after conducting expensive evaluations. We consider four primary categories of metrics, each targeting a specific aspect of the SO process: Sampling Core Metrics, Batch Properties Metrics, Optimization Process Metrics, and Feature Importance Metrics. Our experimental evaluations demonstrate the significant potential of the proposed metrics across different benchmarks.