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MXenes are an emerging family of two-dimensional transition metal carbides and nitrides that have attracted considerable attention due to their exceptional electrical conductivity, surface chemistry tunability, and layered architecture. Despite rapid progress, the rational design of porous MXene-based composites remains fragmented, with limited cross-cutting analyses that connect porous-host selection, structure–property relationships, scalability, and data-driven optimization. In this review, building upon and extending our prior contributions, we present a comprehensive and critical synthesis of organic and inorganic porous materials integrated with MXenes, including metal–organic frameworks (MOFs), covalent organic frameworks (COFs), zeolites, porous polymers, and carbon-based hosts. Beyond a descriptive survey, this work introduces a unified comparative framework that quantitatively contrasts porous hosts in terms of conductivity enhancement, mechanical robustness, manufacturability, and compatibility with machine-learning-assisted optimization. One of the key innovations of this review is the explicit integration of artificial intelligence into porous MXene materials design: we systematically analyze AI-assisted and non-AI design paradigms, propose a closed-loop AI–experiment workflow, and introduce a figure of merit (FoMAI design) to benchmark predictive reliability, data efficiency, and resource utilization across different AI strategies. By linking hierarchical porosity, MXene electronic structure, and data-centric intelligence, this review moves beyond conventional trial-and-error approaches and establishes a conceptual roadmap for predictive, scalable, and application-oriented porous MXene composites. The insights provided here define clear short-, mid-, and long-term research directions for next-generation materials targeting energy storage, electrocatalysis, sensing, electromagnetic interference shielding, and environmental remediation.
Estaji et al. (Sat,) studied this question.