Artificial Intelligence (AI) has significantly impacted software engineering, providing tools and techniques that enhance automation, intelligence, and the development of smarter systems. AI’s role extends beyond simple automation; it encompasses creating intelligent software capable of learning, self-improvement, and adaptation. The Fourth Industrial Revolution, characterized by technological fusion across physical, digital, and biological spheres, has further propelled AI advancements in software engineering. This fusion has led to improved data analysis, pattern recognition, and predictive modeling, transforming various industries, including software engineering. AI-based modeling is vital for software engineering due to its ability to enhance efficiency, accuracy, and scalability. These models automate routine tasks, freeing up human resources for complex problem-solving activities. For example, AI-driven tools predict software defects, automate code generation, and offer intelligent coding recommendations, improving software quality, reducing time-to-market, and lowering development costs. Several AI techniques have been applied in software engineering, including machine learning (ML), neural networks, deep learning, data mining, rule-based systems, fuzzy logic, case-based reasoning, and hybrid approaches. Machine learning is extensively utilized for data analysis and predictive modeling, with supervised, unsupervised, and reinforcement learning as its primary techniques. Supervised learning involves training a model on labeled datasets for predictive modeling, such as identifying potential software defects. In contrast, unsupervised learning works with unlabeled data, useful for clustering similar software projects to identify common features. Reinforcement learning optimizes processes and resource management, such as test case execution sequences, to maximize testing coverage. Neural networks and deep learning models have revolutionized software engineering by processing large volumes of complex data. Convolutional Neural Networks (CNNs) are used in tasks like image recognition, which can be applied to analyze visual data in software engineering. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks are valuable for sequence data analysis, aiding natural language processing in code generation and documentation. Deep learning’s success in various domains hinges on the availability of large datasets and computational resources, though challenges like extensive labeled data requirements and real-time performance limitations remain. AI applications in smart cities demonstrate its transformative potential in improving urban life. AI algorithms analyze data from Internet of Things (IoT) devices to optimize energy use, manage traffic, and enhance public safety. In education, AI-driven profiling tools create personalized learning paths, improving student outcomes, while intelligent tutoring systems adapt to individual learning paces. Healthcare benefits from AI through predictive analytics, personalized medicine, and efficient management, such as automating administrative tasks and assisting in diagnostic processes. The future of AI-based modeling in software engineering lies in addressing technological advancements and ethical considerations. Developing more sophisticated models, enhancing model interpretability, and ensuring data privacy and security are crucial. Furthermore, interdisciplinary research between computer scientists, cognitive scientists, and domain experts can lead to more effective AI solutions. Embracing AI can significantly improve software engineering practices, driving innovation and efficiency in various applications.