The need to employ technology that replaces traditional engineering methods which generate gases that worsen ourenvironment has emerged in an era of dwindling ecosystem owing to global warming has a negative influence on theearth system’s ozone layer. In this study, the exact method of using artificial intelligence (AI) approaches in sustainablestructural materials optimization was investigated to ensure that concrete construction projects for buildings have nonegative environmental effects. Since they are used in the forecasting/predicting of an agro-waste-based green geo-polymer concrete system, the intelligent learning algorithms of Fuzzy Logic, ANFIS, ANN, GEP and other nature-inspiredalgorithms were reviewed. A systematic literature search was conducted to identify relevant studies published in variousdatabases. The included studies were critically reviewed to analyze the types of AI techniques used, the research meth-odologies employed, and the main findings reported. To meticulously sort the crucial components of aluminosilicateprecursors and alkaline activators blend and to optimize its engineering behavior, laboratory methods must be carriedout through the mixture experiment design and raw materials selection. Such experimental activities often fall short ofthe standards set by civil engineering design guidelines for sustainable construction purposes. At some instances, specificshortcomings in the design of experiments or human error may degrade measurement correctness and cause unforeseendischarge of pollutants. Most errors in repetitive experimental tests have been eliminated by using adaptive AI learningtechniques. Though, as an extensive guideline for upcoming investigators in this cutting-edge and developing field ofAI, the pertinent smart intelligent modelling tools used at various times, under varying experimental testing method-ologies, and leveraging different source materials were addressed in this study review. The findings of this review studydemonstrate the benefits, challenges and growing interest in utilizing AI techniques for optimizing geopolymer-concreteproduction. The review identified a range of AI techniques, including machine learning algorithms, optimization models,and performance evaluation measures. These techniques were used to optimize various aspects of geopolymer-concreteproduction, such as mix design, curing conditions, and material selection.

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