Improving the performance and extending the service life of transportation infrastructure is a long standing goal of Federal Highway Administration (FHWA) and the transportation community. Accurate prediction of the mechanical properties of highway materials are indispensable for enhancing the sustainability and resilience of transportation infrastructure since it provides accurate inputs for pavement mechanistic-empirical (ME) design and prediction of pavement distresses, helping to optimally allocate the maintenance needs and reduce testing frequencies which account for costly expenditures. Accurate prediction of materials properties can also reduce the acceptance risks during quality assurance (QA) without conducting extensive testing. Concrete plays an important role in the construction of transportation infrastructure. Developing an empirical and/or statistical model for accurately predicting compressive strength remains challenging and requires extensive experimental work. Thus, the objective of the study was to improve the prediction of concrete compressive strength using ML algorithms. A ML pipeline was proposed in which a two-layer stacked model was developed by combining seven individual ML models. Feature engineering was implemented, and feature importance was evaluated to provide better interpretability of the data and the model. This study promotes a more thorough assessment of alternative ML algorithms for predicting material properties. In addition, the quality of highway materials and construction translate directly to performance. To develop a statistically sound QA specification, the risks to the agency and contractor must be well understood. In this study, a Monte Carlo simulation model was developed to systematically assess the acceptance risks and the implications on pay factors (PF). The simulation was conducted using typical acceptance quality characteristics (AQCs), such as strength, for Portland cement (PCC) pavements. The analysis indicated that specific combinations of contractor and agency sample sizes and population characteristics have a greater impact on acceptance risks and may provide inconsistent PF. The proposed methodology aids both agencies and producers to better understand and evaluate the impact of sample sizes and population characteristics on the acceptance risks and PF. Finally, the use of recycled materials is a key element in generating sustainable pavement designs to save natural resources, reduce energy, greenhouse gas (GHG) emissions and costs. This study proposed a methodological life cycle assessment (LCA) framework to quantify the environmental and economic impacts of using recycled materials in pavement construction and rehabilitation. The LCA was conducted on two roadway projects with innovative recycled materials, such as construction and demolition waste (CDW) and rock dust. The proposed LCA framework can be used elsewhere to quantify the environmental and economic benefits of using recycled materials in pavements.