Process mining integrates computational intelligence, data mining, and process modeling to analyze and optimize real-world business processes. By leveraging event logs from information systems, process mining facilitates process discovery, conformance checking, organizational mining, simulation, and predictive modeling. With the increasing complexity of enterprise systems and data volumes, the adoption of cloud computing has become essential for scalable process mining solutions. This paper investigates the integration of process mining with cloud environments, addressing key challenges such as data preprocessing, interoperability with enterprise applications, and maintaining performance at scale. Furthermore, it examines how AI-driven automation and predictive modeling enhance process optimization, enabling organizations to achieve operational efficiency and regulatory compliance. The study also highlights the capabilities of Pega Process Mining, emphasizing its role in AI-powered insights, cloud scalability, and real-time decision-making. Comparative analysis with existing process mining frameworks is presented to assess strengths, limitations, and potential improvements. Finally, the paper discusses future research directions, including advancements in NLP-driven process analytics, adaptive learning algorithms, and real-time automation for intelligent business process management.