The concrete industry generates approximately 5-10% of global anthropogenic CO₂ emissions from the production of ordinary Portland cement (OPC), and waste such as wood and date palm agro-residues are largely underutilized. This study fills an existing gap in research by examining the mechanical performance of OPC-based concrete with partial replacement of OPC by sawdust ash (SDA), the resulting concrete reinforced with alkali-treated date palm fiber (DPF), and presents age-specific predictive regression models useful for design. The four levels of SDA use (5%, 10%, 20%, and 30% by mass of cement) and two dosage rates of DPF (0.5% and 1% by mass of binder at the optimum SDA rate) were evaluated. To enhance the fiber–matrix bond, DPF was treated with 3% NaOH for 3 hours and then rinsed to neutral pH. Concrete was proportioned per IS 10262 / IS 8112-1989 with a w/c of 0.45 and cured at 23 ± 2 °C and ≥ 95% relative humidity. The cube strength was determined by three replicate specimens for each mix at 28, 56, and 90 days of testing, as per IS 516. Also, the split tensile strength was determined from three replicates of the mix at each point, and the flexural strength was tested simultaneously using three prism replicates. The data are presented as the mean value with SD. At 90 days, it was found that 5% SDA showed compressive strength (45.43 MPa), tensile strength (5.84 MPa), and flexural strength (5.65 MPa), which was comparable with control (43.67 MPa, 5.43 MPa, and 5.17 MPa), while 30% SDA showed a compressive strength reduction of approximately 43%. The optimum mix (5% SDA + 1% DPF) showed increased CS (3.22%), STS (45.86%), and FS (111%) after 90 days when compared with the reference (5% SDA). The models (R² = 0.85-0.96 (linear (SDA)) and R² = 0.99-1.00 (exponential (DPF))) were linear and exponential, with high coefficients of determination, and thus could be used to make predictions within the range studied. The added value is the development of a combined SDA–DPF formulation, along with age-resolved statistical characterization and predictive equations, providing an engineer with a realistic foundation for sustainable structures based on the use of locally available agro-industrial wastes.
