Dr. Ali Juma Alnaqbi is an independent researcher in Civil Engineering, specializing in pavement engineering, asphalt materials, and machine learning applications for infrastructure performance prediction. He earned his Ph.D. in Civil Engineering from the University of Sharjah, United Arab Emirates, where his doctoral research focused on developing data-driven and hybrid machine learning models for predicting pavement performance indicators, including IRI, cracking, spalling, punchouts, rutting, and friction, using data from the LTPP program. Dr. Alnaqbi has published more than 30 peer-reviewed journal articles in Q1 and Q2 Scopus-indexed journals, addressing topics such as pavement performance modeling, multi-distress prediction, feature importance analysis, and optimization-enhanced machine learning frameworks. His research integrates advanced techniques including Random Forest, Gradient Boosting Machines, Support Vector Regression, Artificial Neural Networks, Gaussian Process Regression, and metaheuristic optimization algorithms such as Genetic Algorithms and Particle Swarm Optimization. He has completed over 360 verified peer reviews for international journals indexed in Web of Science and has received the Sharjah Sustainability Award twice in recognition of his contributions to sustainable infrastructure research. His Master’s thesis investigated styrene–butadiene–styrene (SBS) modified asphalt binders to meet the traffic loading and climatic conditions of the UAE.