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A text’s grammatical errors (GEs) are passages that are broken down to accept grammar standards. Resolving grammatical faults and inconsistencies in a document is the aim of GEs. Different strategies can focus on varying textual spans, from individual phrases to whole documents. Common GEs include improper word choice, punctuation, and syntax. The development of natural language processing (NLP) has fundamentally changed the way textual data is analyzed and processed, leading to notable advancements in automated grammatical mistake detection systems. In this study, we proposed a novel starling murmuration-optimized dense recurrent neural network (SMO-DRNN) model for the detection of English grammar errors. In this study, we collected text samples that were analyzed by marking different types of GEs. Automatic reading also involves converting textual data from English compositions into numerical values for further computation. Data pre-processing techniques include tokenization, stop word removal, stemming, and lemmatization. To extract relevant information from the pre-processing data, term frequency-inverse document frequency (TF-IDF) feature extraction was used for accurate grammar detection. The proposed approach is compared to the traditional algorithms. The overall results show that the proposed approach performed better than the existing method in terms of accuracy and loss, ROC (0.95), recall (92%), precision (96%), and F-0.5 score (94%), for identifying English grammar errors. The suggested method effectively combines current NLP strategies to offer a highly accurate method for identifying grammar mistakes in English.