Effective manuscript evaluation plays an important role both in publishing and professional communications, including education. Proper and effective means of evaluation can reduce time-consuming processes, guarantee quality outputs, and lead to well-informed decisions. The study applies the structured approach as a means of providing input on how to improve accuracy and efficiency in assessment. For this, QDA supported by ZO and MRFO algorithms was used. Such advanced algorithms as ZO and MRFO were combined with the QDA model to enhance its performance regarding the evaluation of writing scores. This was achieved by integrating ZO and MRFO algorithms into the QDA model, which resulted in a high level of improvement in accuracy and efficiency. After elaborate analysis, the QDZO emerged as the best-performing model with an accuracy value of 0.938. The QDMR came closest to QDZO with an accuracy value of 0.911, while the QDA model, being a baseline model without the incorporation of any of the optimization algorithms, was the worst, represented by an accuracy value of 0.876. The lower accuracy value here indicates that the baseline model does not perform well, which means an advanced optimization algorithm should be integrated to enhance its performance.
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