Waste Cooking Oil (WCO), Soy Oil (SO), and Wastewater Sludge (WWS) have great potential to increase reclaimed asphalt pavement (RAP) content for economic and environmental benefits. This study explored the effects of SO and WCO on rutting, fatigue cracking, and low-temperature cracking performance of binders and Hot Mix Asphalt (HMA) with high RAP content. The potential effect of WWS on the performance and compaction efforts of high RAP content mixes at a 10 °C (50 °F) lower compaction temperature than the control compaction temperature was also investigated. The results indicated that 85% of the RAP binders can be incorporated while maintaining similar performance compared to the control by using 15% SO or 12.5% WCO as a rejuvenator with 2.5% virgin binder. Adding 1% WWS by weight of the total binder improved the binder’s rheological properties, the mix’s cracking performance, and the mix’s density at lower compaction temperatures.

Fatigue crack deterioration in flexible pavements results from structural loading, traffic demand, material aging, and climatic exposure; yet, Ethiopian pavement models remain largely empirical, with little mechanistic foundation. This study develops a hybrid mechanistic–empirical and artificial neural network framework to forecast fatigue crack progression along a five-kilometer segment of the Woldia–Jeneto road in northern Ethiopia, built in 2015 and assessed after ten years of service. ERAPave layered elastic analysis computed critical horizontal tensile strain at the asphalt base, using the ERA manual recommendation of the Australian fatigue criterion for tropical areas, deriving cumulative damage indices via Miner’s rule. These outputs, alongside material properties, soil indices, traffic, climate, and temporal variables, formed an 18-feature input vector, which was trained using Latin–Hypercube Sampling and leave-one-out cross-validation under data-scarce conditions. Critical tandem-axle loads of 200.2 kN produced tensile strains of 182.7–199.83 με and damage ratios of 0.39–0.76 within fatigue lifetimes of 10.46–20.12 million ESALs, exceeding the 7.93 million ESAL design threshold. The model achieved R² = 0.9997 and MAPE = 1.64%; these figures reflect five-station training conditions and synthetic augmentation rather than unconditional generalization accuracy. Ten-year forecasts place Station 5 at structural failure within three years, supporting evidence-based pavement maintenance planning.