Early and accurate diagnosis of kidney cancer is important for effective treatment planning and improved patient prognosis. This work proposed a strong ensemble of deep learning for binary classification of kidney histopathological images into tumor and normal classes. The dataset employed was obtained from the publicly accessible Multi Cancer Dataset on Kaggle, with all images resized to 128×128 pixels to ensure consistency. We implemented a bagging ensemble strategy by training three distinct convolutional neural network models, each based on a pre-trained ResNet50 architecture with frozen base layers. Each model was trained on a different subset of the training data to promote diversity within the ensemble. The predictions of each model were aggregated with soft voting for the final prediction. Based on the evaluation of the test set, our ensemble achieved an accuracy of 94.46% with high precision, recall, and F1-scores. Our results demonstrate that the bagging ensemble effectively has robustness in automated kidney cancer detection and has potential as a decision-support tool in clinical practice. The proposed method not only reduces variance and improves classification stability but also highlights the effectiveness of using ensemble learning with transfer learning for histopathological image analysis, as we note that it was a successful collaboration.