Nilesh Hanuman Dhannaseth is an Assistant Professor in the Department of Information Technology at Smt. Radhikatai Pandav College of Engineering (SVPCET), Nagpur, India, with over 13 years of combined industrial and academic experience. He holds an M.Tech. in Computer Science and Engineering from Vishwakarma Institute of Technology, Pune, and a B.E. in Computer Science and Engineering from JDIET, Yavatmal. He is also UGC-NET qualified in Computer Science and Applications (2019) and qualified GATE 2009 with a 91.36 percentile. Before entering academia, Mr. Dhannaseth worked as a Web Developer at Cognizant Technology Solutions, where he contributed to enterprise software projects using Java, JavaScript, HTML, CSS, JSP, AJAX, and responsive web technologies. His academic and research interests include Artificial Intelligence, Machine Learning, Large Language Models (LLMs), Software Engineering, Cloud Computing, Soft Computing, Operating Systems, Geographic Information Systems (GIS), and Intelligent Software Architecture. He currently mentors several AI-based student research projects, including brain tumor detection, rainfall prediction, stock market forecasting, and intelligent healthcare applications. Mr. Dhannaseth has authored numerous peer-reviewed publications in leading international journals and conferences on topics such as Vision AI, Rainfall Prediction, Large Language Models, Speech and Writing Assistive Technologies, AI in Agriculture, and Data-Driven Predictive Analytics. He actively participates in research, curriculum development, workshops, and technical conferences while serving in various academic and institutional leadership roles. His commitment to advancing AI-driven software engineering continues to contribute to the development of intelligent, scalable, and socially impactful computing solutions.        

Abstract Title Bridging Intelligent Software Architecture, AI/ML Engineering, and Future Technologies Abstract Modern software systems are rapidly evolving from conventional architectures to intelligent, AI-driven ecosystems capable of supporting automation, real-time decision-making, and scalable digital transformation. This paper explores the convergence of intelligent software architecture, Artificial Intelligence (AI), Machine Learning (ML), and emerging technologies to develop adaptive, resilient, and sustainable software systems. It emphasizes AI-native architectural design, where machine learning models are embedded as core components rather than isolated services, supported by robust MLOps practices that integrate continuous model deployment, monitoring, and lifecycle management within the Software Development Lifecycle (SDLC). The study highlights the importance of data-centric architectures, edge-cloud convergence, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), multi-agent systems, digital twins, and quantum computing as key enablers of future software ecosystems. It also examines the architectural challenges associated with scalability, security, privacy, explainability, and regulatory compliance, advocating for Responsible AI Governance through Explainable AI (XAI), federated learning, and robust cybersecurity mechanisms against adversarial attacks and data poisoning. Furthermore, the paper demonstrates the transformative impact of intelligent software architectures across healthcare, finance, smart manufacturing, IoT, and smart city applications. By integrating advanced AI engineering principles with cloud-native software design, the proposed architectural vision supports the development of ethical, transparent, and highly scalable intelligent systems that drive innovation while delivering sustainable societal benefits.