<p>Mechanoluminescence (ML) is bringing a paradigm-shifting for next-generation light-based human–robot interaction. However, the overlooked character of ML temporal dynamic response remains a critical barrier to overcoming the limitation of mechano-optical conversion efficiency. Here, by resolving the dynamic interplay among stimuli rate, interfacial charge accumulation and ML performance of three typical materials, like ZnS:Cu²⁺, SrAl₂O₄:Eu²⁺,Dy³⁺, Y₃Al₅O₁₂:Ce³⁺, the cognition of ML has been deeply understand. Obviously, the ML performance is predominantly governed by the cross-coupling of stimuli rate and stimuli time rather than absolute stress magnitude. For the first time, the optimal stretching stimulation rate for commercial ZnS:Cu²⁺, SrAl₂O₄:Eu²⁺,Dy³⁺ and Y₃Al₅O₁₂:Ce³⁺ are respectively determined as ~ 10.3 Mpa/s, ~ 11.0 Mpa/s, ~ 31.9 Mpa/s, which is of great significance for obtaining high-performance ML behavior, and an ubiquitous ML hysteresis phenomenon is demonstrated originating from a time-consuming mechano-electro-optical conversion process even existing in trap-controlled SrAl₂O₄:Eu²⁺,Dy³⁺. Moreover, a qualitative relationship for ML brightness (MLB), stimuli rate (<i>sr</i>), stimuli time (<i>st</i>), inherent interfacial triboelectricity coefficient (<i>iitre</i>) and relative interfacial triboelectricity coefficient (<i>ritre</i>) is established as <i>MLB</i> = <i>f</i>(<i>sr</i>)*<i>g</i>(<i>st</i>)*<i>p</i>(<i>iitre</i>)*<i>q</i>(<i>ritre</i>) for guiding the design of ML elastomers. For instance, based on this equation, a topology-optimized Y₃Al₅O₁₂:Ce³⁺@polydimethylsiloxane (PDMS) elastomer is engineered, achieving unprecedented 693 times brighter emission, 78% lower stress threshold and 20% lighter weight, which is successfully applied in remote control (~ 450&#xa0;m) of quadruped robot. Three main contributions of this work include: (i) demonstrating the influence law of temporal dynamic stimulation on ML performance. (ii) resolving long-standing mechano-optical asynchrony debates. (iii) establishing a universal guideline for designing high-performance ML platforms.</p>