<p>Increased usage of cloud storage for image data requires efficient image deduplication for optimal cloud storage usage with quick and accurate retrieval of images. Existing image deduplication techniques use hash functions, extracted feature or deep learning techniques. Bloom filter can identify potential matches helping to reduce computational overhead in image deduplication check in cloud storage. Bloom filters can be effectively integrated with hashing and feature extraction techniques. While Bloom filters are space-efficient and can offer fast lookups with some tolerance for false positives, a HashMap is better for scenarios that demand exact lookups, accurate results, and support for deletion. HashMap based image deduplication approach is proposed in this paper for cloud environment that offers a fast and scalable approach with accurate hash-based verification. This work highlights that the proposed approach is scalable that can efficiently handle large-scale image comparisons without limiting the number of images that can be compared against the input image. HashMaps enable quick indexing and retrieval of image hash values, which significantly reduces the computational overhead and storage redundancy by detecting duplicates efficiently. They store each key explicitly along with its value, ensuring exact membership testing thus overcoming the limitation of false positives in Bloom filters. This approach leverages hashing algorithms to create unique identifiers for images, facilitating fast lookups and exact or near-duplicate detection in dynamic cloud environments. Experimental results prove that the proposed image deduplication reduces storage requirements and processing time while maintaining accuracy in duplicate check compared to Bloom filter in image deduplication process.</p>