AI ML and cloud computing: exploring models, challenges and opportunities

The collaboration between Artificial Intelligence (AI) and Machine Learning (ML) in the landscape of Cloud Computing (CC) is fundamentally redefining the techniques that enterprises apply in terms of data processing, security, cost-optimization, scalability, and resource oversight. Consequently, cloud platforms provide the essential infrastructure and flexibility necessary for AI and ML algorithms to analyze vast datasets, while automation propelled by AI and ML models enhances cloud services by optimizing performance, reducing latency, and forecasting demand. This intersection not only facilitates dynamic scalability and efficient resource allocation but also unveils revolutionary prospects, including intelligent automation, self-healing systems, and adaptive security frameworks. This state-of-the-art review provides researchers with current trends, challenges in this rapidly growing field and points towards research gaps and unexplored research directions. This literature review explores the synergistic interplay between AI, ML, and Cloud Computing, highlighting significant advancements, intrinsic challenges, and potential opportunities across various sectors. We examine the different techniques that enable AI and ML algorithms to enhance cloud applications, focusing specifically on domains like automated decision-making, optimization, operations, scheduling and security. This review paper aims to provide a comprehensive perspective of the current state of this convergence, the challenges it faces, and the opportunities it presents for the future.