The current study tries to cut carbon emissions by using various waste materials in place of cement, including sugarcane bagasse ash (SCBA), ground granulated blast furnace slag (GGBFS), and ladle furnace slag (LFS), individually and in a combined form also, which has not been studied yet. In the same context, effort was made to utilize the maximum amount of waste materials as the replacement of cement to create a sustainable environment. Besides this, another aim is checking the performance of these waste materials as binding materials with respect to compressive strength for sustainable rigid pavement construction without activating them or using any activating solution. For this purpose, the compressive strength test is done for GGBFS, LFS, and SCBA, and later on, the artificial neural network (ANN) technique is also used to check the novelty of results in a broad way. For the same purpose, M40 grade concrete was made by incorporating different selected waste materials in a varying proportion ranging from 0 to 35\%. Based on the results obtained from the compressive strength test for different curing periods, i.e., 7, 14, and 28 days, it was observed that the GGBFS, LFS, and SCBA can be utilized individually up to 15\%, respectively. Another observation made from the findings was that the use of LFS and SCBA in the individual form up to 20\% was found to be possible as the maximum reduction in strength was found to be up to 2.63\%. However, the cumulative impact of all these waste products was also examined. Based on the data, it was concluded that the best outcomes would arise from using these additives in combination to replace cement in the mix by up to 30\% (i.e., without compromising the required characteristics of concrete), which will be proved as an aid to the environment and the society also. Besides this, the fluctuation in the compressive strength value of concrete mixes after integrating various waste materials was also examined in order to construct a model using the ANN approach. The model's outcomes suggest that the ANN model does a good job of forecasting the compressive strength of concrete.