Although healthcare documentation is increasingly dependent on speech-based clinical interactions, general-purpose Automatic Speech Recognition (ASR) and Large Language Models (LLMs) lack the domain adaptation, structured control and interoperability guarantees required in regulated medical environments. These limitations often result in transcription errors, hallucinated content, and limited alignment with standardized coding systems. This paper introduces MedicaLLM, a multilingual, end-to-end framework integrating domain-adapted ASR, LLM-based structured report generation, and ontology-driven semantic enrichment within a modular architecture for clinical documentation. MedicaLLM combines medical interview transcription with structured report generation, summarization, and error correction; Named Entity Recognition (NER); and Medical Entity Linking (MEL) to align with standards such as SNOMED-CT and ICD-10. Deployed as a secure software as a service (SaaS) platform with REST API integration, MedicaLLM aims to reduce the administrative burden, improve the quality of documentation, and enhance semantic interoperability across healthcare systems, all while maintaining computational efficiency and clinical reliability.