ABSTRACT: The growing need for personalized digital assistants has led to the development of systems that can effectively store, process, and retrieve user-specific data through natural language interactions. This paper introduces a Personal Memory Assistant (PMA), designed to handle both voice and text inputs, enabling users to store queries and retrieve relevant information when required. The system incorporates a range of Natural Language Processing (NLP) methods, such as tokenization, stopword removal, and Term Frequency-Inverse Document Frequency (TF-IDF), to efficiently analyze and interpret user inputs. For voice- based inputs, a speech-to-text mechanism is employed, offering users the flexibility to switch between voice and text seamlessly. User queries and data are stored in a cloud environment using Firebase, which ensures real-time synchronization and scalability of the stored information. Upon receiving a query, the system applies TF- IDF to match the input with previously stored data, facilitating accurate and contextually relevant retrieval. This approach allows the system to manage structured and unstructured data efficiently. By combining advanced NLP techniques with cloud based storage and real-time data processing, the PMA delivers personalized responses, enhancing user engagement and interaction. The paper demonstrates the potential of integrating cloud technologies and NLP methods to improve the functionality of digital assistants in providing context-aware, tailored responses. Keywords: Personal Memory Assistant (PMA), Natural Language Processing (NLP), Tokenization, Stopword Removal-TF- IDF, Speech-to-Text, Firebase, Cloud.