<p>Technology having advanced tools, method and system designed to resolve real world issues and advancing contemporary technological generation. Computing domains like parallel, distributed, cluster, and grid computing offer various benefits focusing on time, cost, and fast execution time and other QoS needs. Cloud commuting known for its scalability and flexibility which makes it promising for multiple domains such as medical, finance, stock exchange and AI. Its dynamic resource provisioning is crucial for machine learning, enabling large-scale data processing at lower costs. A key research area in cloud computing is workflow scheduling, optimizing tasks through Directed Acyclic Graphs (DAGs). Artificial Intelligence is a vast umbrella that embraces multiple domains of technologies, including Machine Learning (ML) and Reinforcement Learning (RL). In this paper, a deeper understanding of ML and RL is presented, highlighting their relevance and applications in the context of the cloud workflow scheduling. Where, Machine learning enhances this process by improving decision-making and automation. Integration of AI-driven technologies in Cloud workflow scheduling brings intelligent system automation and adaptability that traditional approaches lack. Furthermore, AI enables predictive scheduling system and proactive management by forecasting resource deadline or failures and taking corrective actions in advance. It also enhances reliability and fault tolerance through advanced strategies. In this paper we have discussed workflow model with its computing model in cloud environment in details. This paper also explores twenty-four AI-driven workflow scheduling approaches in cloud environment analysing performance of each algorithm along with the QoS parameters employed and limitations and exploring Tool and Scientific Workflow used for the algorithm’s simulation. Additionally, it outlines how AI's growing role in cloud platforms can shape the future of workflow scheduling, addressing the limitations of the models.</p>