Recent advancements in Remote Sensing (RS) have created challenges in data storage, retrieval, and privacy. Existing Content-Based Image Retrieval (CBIR) systems are useful but often face limitations related to hypersensitivity towards remote sensing data in the cloud, scalability, and security. This article presents SecureRS-CBIR, a privacy-preserving framework for remote sensing image retrieval combining deep learning with multi-level encryption. The system uses three CNN models (VGG16, ResNet50, and DenseNet121) for feature extraction and implements encryption through image division, texture extraction, subblock shuffling, and color encryption. Experiments on the Aerial Image Dataset show VGG16 achieving 96% validation accuracy, with ResNet50 and DenseNet121 at 95% and 94% respectively. DenseNet121 excelled at DenseResidential classification (41/42 correct) with minor confusion between Beach and Desert categories. The framework successfully balances security with retrieval efficiency, maintaining privacy through robust encryption while enabling accurate content-based searches, providing a scalable solution for secure image retrieval in cloud environments. This work offers a new approach for remote sensing image retrieval by enabling efficient searching in large-scale datasets while addressing privacy concerns in cloud environments, thereby contributing to the relevant literature.