A major difficulty in healthcare is the detection of breast cancer, which necessitates the use of robust models for precise diagnosis. To improve the accuracy of breast cancer detection, this work presents an improved feature extraction model that makes use of the Spider Monkey technique. A variety of classifiers, such as Support Vector Machine, Random Forest, Naive Bayes, and Decision Tree, are used to validate the suggested model. By choosing the most discriminative features, the Spider Monkey method helps to optimize the classification process. The model's efficacy in achieving high accuracy and reliability in breast cancer detection is demonstrated by experimental validation. This work highlights the potential of nature-inspired optimization in feature extraction and advances complex strategies for better medical diagnostics.