Early diagnosis can alter the survival conditions in patients. Machine learning techniques that can help now are being developed to provide an efficient diagnosis for individuals who have breast cancer. In this study, an ensemble model is proposed with a dataset consisting of two classes: cancerous images and non-cancerous images. This dataset has been used as input after feature extraction to train a number of classifier models, among which Artificial Neural Network (ANN), Logistic Regression, Support Vector Machine (SVM), and Naive Bayes have been trained and evaluated individually. The various models trained gave accuracy percentages as follows: 94, 95, 94.38, and 94.12%. Then, ensemble implementation combining the outputs of these individual classifiers has been designed for better performance in predictability. All the predictions from Logistic Regression, SVM, Naive Bayes, and ANN models generate the final prediction through a weighted average. This leads to an accuracy of 96.71% for the ensemble model than its peer models. Hyperparameter tuning of the ensemble model with boosting techniques improves the performance. The whole procedure for optimizing the model parameters and also boosting weak learners gives an accuracy of 97.56%. Results indicate that the ensemble model, particularly when boosted and tuned up for proper hyperparameter settings, may offer better performance for breast cancer detection than individual classifiers. This technique is promising to be used as an early and reliable tool for cancer diagnosis using the strength of multiplicity to achieve superior performance.