Abstract: AI-driven developments have overshadowed the advances in algorithmic computing since the early 21st century, providing opportunities for exponential growth. The key competencies addressed were centered around replacing sequential and heuristic operations in repetitive applications by process and decision automation, through both deterministic algorithms (machine learning/deep learning, ML/DL) and probabilistic model predictions (support vector machine, generalized regression neural network). The synergies of machine learning and generative adversarial neural network (ML/GAN) products have transformed the conventional business lines of services into the blue ocean sectors of businesses, across content creation (image, voice, language, music, and videos), forecasts/recognitions/intelligent processors (image forecast/photo recognitions on collections and photos, emulation and replacements on voice/captions/texts/styles, forecasting).Millennial cloud computing supports big data exponential growth by providing high-performance parallel processing, multiple tenants load balancing, practical real-time data processing, and substantially lower costs. Grouping previously single-server units and sharing servers digital systems have relieved the Lilliput effect and rescued Moore's Law. Ushering in its disruptive economies of scale, cloud AI, and AI-on-cloud research and emerging applications, cloud computing has established its niche services value propositions over conventional value computing; in relationship with it, beyond the traditional hardware and software incentives and synergies, broadening the foundation of tomorrow's big cloud data centers, they are showing impactful relevance in reducing carbon footprints, supporting climate actions, contributing to achieving the United Nations (UN) Agenda for Sustainable Development 2030 and United Nations Human Rights SDG16.

Keywords: AI-Driven Enhancements in Cloud Computing: Exploring the Synergies of Machine Learning and Generative AI, Industry 4.0, Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Smart Manufacturing (SM),Computer Science, Data Science,Vehicle, Vehicle Reliability

| DOI: 10.17148/IARJSET.2022.91020