Sentiment analysis of medication evaluations uses Natural Language Processing (NLP) techniques to analyze users' thoughts and feelings about medications (treatments) they have received, helping evaluate the effectiveness of therapy. Machine learning aids in evaluating medication effectiveness; however, it has limitations due to the context-dependent nature of medication-related sentiment. Even though deep learning provides a better ability to capture nuances in language, deep learning models will require larger datasets and considerable computational resources. A Hybrid Bidirectional Gated Recurrent Unit-Convolutional Neural Network (Bi-GRU-CNN) is proposed for sentiment analysis of medication evaluations to address these shortcomings. The evaluation of medicines from patients involved collecting reviews and compiling them into a structured dataset as the first step. The process uses various methods to preprocess the reviews, including lowercasing, stop-word removal, and punctuation removal. SentiWordNet is used to assign sentiment polarity to reviews after they have been pre-processed, classifying them as neutral, negative, or positive. The attention-based Bidirectional Long Short-Term Memory (Bi-LSTM) model is then applied for part-of-speech tagging to extract semantic information, and a dependency tree parser that uses Global Vectors for Word Representation (GloVe) to extract syntactic information. Bidirectional Encoder Representations from Transformers (BERT) are then used to fuse the two features into a single representation. A hybrid BiGRU-CNN model is then used to classify sentiment and accurately predict the polarity of drug reviews. The CNN layer captures many local patterns, and the BiGRU layer processes sequential dependencies in both directions. The features are combined and utilized in a series of dense layers to create classifications. The proposed method achieved an overall Accuracy of 97.50%, a False Omission Rate of 4.30%, and a selectivity of 94.20% in sentiment analysis of drug reviews. The proposed method improves contextual understanding and feature extraction for a more granular understanding of sentiment.