This study investigates the creation of eco-efficient gypsum plaster composites that integrate untreated textile waste (TW) with a hybrid experimental-computational methodology that combines machine learning (ML) prediction with multi-objective metaheuristic optimization. Prismatic specimens of 40 × 40 × 160 mm were fabricated with TW concentrations varying from 0% to 1% and water-to-plaster (W/P) ratios between 0.55 and 0.70. The composites were evaluated for rheological parameters (initial and final setting times, spreadability), durability (capillary absorption), mechanical performance (compressive strength CS and flexural strength FS), and thermal conductivity (TC). The results indicated that TW markedly affected plaster performance: a 0.75% TW addition produced the maximum compressive strength (11.67 MPa) and flexural strength (4.17 MPa), while thermal conductivity reduced from 0.20 to 0.15 W/m·K, hence improving thermal insulation. Nonetheless, workability was diminished—spreadability decreased from 210 mm to 130 mm, and initial setting time reduced from 7 to 3 min—underscoring a trade-off. A deep neural network enhanced by the Improved Grey Wolf Optimizer (DNN–IGWO) attained superior prediction accuracy (R² > 0.95), proficiently simulating nonlinear relationships between TW and W/P ratios. A genetic algorithm (GA) produced a Pareto front of 71 non-dominated solutions, optimizing strength, thermal performance, and workability. Optimal formulations were achieved at W/P ratios of 0.55–0.65 and TW levels of 0.25–1 wt%, enabling the development of high-performance, sustainable gypsum composites derived from industrial textile by-products. The findings support the incorporation of recycled textiles in construction and illustrate how data-driven optimization can inform the advancement of sustainable gypsum-based materials utilizing industrial by-products.
Bouzeroura et al. (Sun,) studied this question.