The reduction in compressive strength (CS) of cementitious composites incorporating waste plastic is the main concern limiting its applicability in the building sector. Using industrial wastes as cement substitutes to enhance the CS of plastic mortar is a sustainable approach. This study used fine powdered waste materials such as silica fume (SF), glass powder (GP), and marble powder (MP) in plastic-based mortar for their effectiveness in enhancing CS. Plastic mortar samples were cast using shredded plastic waste in 5-25% contents as sand replacement by mass, and their 28-day CS was recorded as a reference. SF, GP, and MP were utilized in plastic mortar mixtures separately in proportions of 5-25%, with a 5% increment, substituting cement by mass. These waste materials were also used in combinations of two (SF+GP, SF+MP, and GP+MP) and three (SF+GP+MP) in plastic mortar mixtures. Moreover, prediction models were built using the experimental database for the CS of plastic mortar. Gradient boosting and bagging ensemble machine learning (ML) techniques were chosen for model development. The decrease in CS was limited by substituting SF, GP, and MP for cement in plastic mortar. It was determined that the most effective replacement levels for SF, GP, and MP in plastic mortar mixtures, according to the strength enhancement, were 15%, 10%, and 15% by cement mass, respectively. The ML models closely matched experimental results, and in terms of R2 and error evaluations, bagging model outputs were more accurate than gradient boosting. The gradient boosting and bagging models had R2 of 0.89 and 0.94, respectively, with average absolute errors of 0.87 and 0.65MPa.