The rapid evolution of internet slang creates crucial challenges for recognition as well as contextual interpretation and ranking across digital platforms. The paper presents an enhanced version of the SLANGZY (Gupta et al. in Progress in artificial intelligence. Springer, 2019) [1] model for the extraction and ranking of slang terms from Reddit and YouTube. Key improvements include the introduction of fuzzy logic with Gaussian membership functions for slang factor computation, the use of Fuzzy Inference Systems (FIS) for handling the fuzzified inputs and building accurate rankings, integration of contextual embeddings (BERT) for a better understanding of slang meaning, and additional features based on TF-IDF for enhanced syntactic analysis. The model automates the data gathering, cleaning, and contextual enrichment of slang by using the Urban Dictionary API for its definitions and examples. Upvotes, upvote-to-downvote ratios, and definition length are fuzzified variables in calculating a single slang factor, ranking terms by their prominence and frequency. Clustering techniques and t-SNE visualization are used to display the distribution of slang across platforms. Although the model has made good progress in slang detection as well as ranking, it reveals scope for improvement in terms of accurate classification into specific groups. Modular design of the advanced SLANGZY model provides greater adaptability and precision in the understanding of changes in usage and shifts in culture within online communities. This development leaves broad scopes for future applications in sentiment analysis, social media monitoring, and the study of changes in language in digital space.