The integration of Artificial Intelligence (AI) into next-generation smart engineering systems has emerged as a transformative force reshaping modern industrial, manufacturing, and infrastructural ecosystems. AI-driven technologies such as machine learning, deep learning, computer vision, and intelligent automation are increasingly embedded into engineering systems to enhance efficiency, adaptability, predictive capability, and autonomous decision-making. These systems are widely applied in domains such as smart manufacturing, intelligent transportation, predictive maintenance, energy optimization, robotics, and cyber-physical systems.This study explores the role of AI integration in next-generation smart engineering systems and its impact on system performance, operational efficiency, and decision-making accuracy. The research examines how AI enhances system intelligence through real-time data processing, predictive analytics, and adaptive control mechanisms. A structured methodology involving qualitative analysis, case study evaluation, and survey-based data collection is adopted to assess the effectiveness of AI-enabled engineering systems.The findings reveal that AI integration significantly improves system reliability, reduces operational costs, and enhances predictive maintenance capabilities. However, challenges such as data security risks, high implementation costs, and lack of skilled workforce remain critical barriers. The study concludes that AI will play a foundational role in the evolution of future engineering systems by enabling autonomous, self-learning, and highly efficient smart infrastructures.