This paper presents our system, InsightBuddy-AI, designed for extracting medication mentions and their associated attributes, and for linking these entities to established clinical terminology resources, including SNOMED-CT, the British National Formulary (BNF), ICD, and the Dictionary of Medicines and Devices (dm+d).To perform medication extraction, we investigated various ensemble learning approaches, including stacked and voting ensembles (using first, average, and max voting methods) built upon eight pre-trained language models (PLMs). These models include general-domain PLMs—BERT, RoBERTa, and RoBERTa-Large—as well as domain-specific models such as BioBERT, BioClinicalBERT, BioMedRoBERTa, ClinicalBERT, and PubMedBERT.The system targets the extraction of drug-related attributes such as adverse drug effects (ADEs), dosage, duration, form, frequency, reason, route, and strength.Experiments conducted on the n2c2-2018 shared task dataset demonstrate that ensemble learning methods outperformed individually fine-tuned models, with notable improvements of 2.43% in Precision and 1.35% in F1-score.We have also developed cross-platform desktop applications for both entity recognition and entity linking, available for Windows and macOS.The InsightBuddy-AI application is freely accessible for research use at https://github.com/HECTA-UoM/InsightBuddy-AI.