Concrete innovations of the highest caliber have contributed in the development of cementless geopolymer concrete (geo-con) that is sustainable and eco-friendly. Various by-products/wastes (such as fly ash (FA), ground granulated blast furnace slag (GGBS)) generated by various industries (such as coal-fired power generating industry, iron industry) have come to life in geo-con, appearing to be a hopeful strategy from an environmental standpoint. A lot of research has been done in order to develop geo-cons from wastes, and one example may be found in sugarcane waste. With increasing demand for sugarcane by-products, the wastages while processing these by products are also increasing. A major chunk of this wastage is sugar cane bagasse ash (SBA) which is produced by burning sugarcane bagasse in the factory to add up to the environmental pollution. Keeping these aspects in mind, partial utilization of SBA along with FA and GGBS was considered in this study. Wherein FA and GGBS are well established precursors for the development of geo-con. This study investigated the effects of SBA (0-15\%) and GGBS (0-40\%) addition on Fly ash-based geo-con (FA-GPC) as a substitute for FA at different curing temperatures (60-90 \& DEG;C) and sodium hydroxide/sodium silicate (NaOH/Na2SiO3) ratios (1:1-1:2.5). The results demonstrated that the workability of resultant geo-con reduced as percentage of SBA and GGBS increased. Similar trend was also observed with increasing NaOH/Na2SiO3 ratios. Furthermore, the compressive strength results reveal that FA to SBA ratio has negative effect on compressive strength but combined effect of SBA and GGBS ratio along with FA has positive effect on compressive strength. Conduction of experiment for compressive strength is a drudgery process. To eliminate that, two major statistical methods, multiple linear regression analysis and polynomial regression analysis, with accuracy of + 5.05 to - 3.05\% and + 4.94 to - 3.40\%, respectively, were developed. An artificial neural network model with an accuracy of + 0.44 to - 3.10\% was also developed. Statistical parameters such as MSE and R-2 were used to further understand prediction accuracy and compare the models. The results show that the ANN model predicts compressive strength more accurately than other models. This study intends to describe a unique process for using sugarcane industrial waste in the form of SBA, with results demonstrating good compressive strength for geo-con. Also, the developed models for compressive strength prediction can be used effectively to reduce experimental drudgery.