<![CDATA[The implementation of stacked ensemble models (SEMs) remains widespread because they combine multiple learning algorithms into one predictive system. SEM implementations continue to struggle with accuracy limitations and overfitting problems and high computational expenses and poor interpretability issues. This review examines 269 scholarly articles from 2020 to 2025 to determine the main technical problems and their associated optimization solutions. The research presents a method engineering-based modular three-stage system which includes pre-processing, processing, and post-processing phases. The three stages address particular weaknesses by improving data quality and features, optimizing models and their parameters, and enhancing interpretability and adaptability. The framework connects SEM pipeline phases to these strategies which enable context-specific reusable design for condition-aware implementation. This research provides a systematic framework to match SEM optimization approaches with development stages which helps develop strong ensemble models that are efficient and interpretable for practical use.]]>
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