This paper presents a systematic review of contemporary health informatics architectures, focusing on the paradigm shift from static fitness applications to dynamic, generative ecosystems. Traditional fitness platforms provide rigid, pre-defined templates that fail to adapt to a user\'s evolving biomechanical constraints, while enterprise gym management systems remain operationally isolated from the actual health metrics of their members. To address these limitations, recent research has explored the integration of Large Language Models (LLMs) to synthesize personalized workout and nutritional regimens dynamically. This survey evaluates the state-of-the-art in automated fitness recommendation engines and gym operational frameworks, analyzing the trade-offs between legacy rule-based systems and modern generative APIs. By establishing a taxonomic classification of existing technologies, this review exposes critical gaps in cross-platform orchestration, highlighting the necessity for unified architectures—such as the proposed FitCore paradigm—that seamlessly combine conversational AI with enterprise resource management to maximize user progression and operational efficiency.