In today's complex business landscape, maintaining high standards of operational efficiency and reliability in both business and technology environments is critical. Managing distributed systems across on-premises, cloud, and hybrid environments while ensuring seamless business operations, such as payment repairs and FX reconciliation, requires robust, real-time observability. This paper introduces a comprehensive observability framework that integrates probabilistic models, machine learning (ML) algorithms, large language models (LLMs), and rule-based systems. This framework dynamically guides event data, structured and unstructured, through intelligent paths to ensure accurate categorization and timely issue resolution, ultimately enhancing key business and technology KPIs. Empirical validations from hypothetical and real-world scenarios strengthen its applicability, and a detailed implementation guideline provides actionable insights for deploying the framework. Additionally, a clearer breakdown of framework components, scalability considerations, diverse datasets, and real-world case studies are presented for a more comprehensive understanding.