India experiences substantial post-harvest losses of fruits and vegetables due to inadequate storage infrastructure, limited real-time quality monitoring, and lack of efficient market connectivity. To address this challenge, we propose KrishiTech—a digital framework under development that leverages machine learning to assist farmers in managing perishable produce. The proposed system enables farmers to capture and upload images of crops, which will be analyzed by a deep learning model to assess freshness and estimate remaining shelf life. Based on these predictions, the framework is designed to suggest nearby cold-storage facilities, identify potential processors for value addition, and provide a built-in community directory where farmers, storage owners, and processors can directly interact. This community support fosters peer-to-peer learning, resource sharing, and faster linkage between producers and service providers. Additionally, farmers will receive timely notifications as produce nears expiry, allowing proactive storage or sale decisions. The architecture combines a ReactJS-based user dashboard, Node.js/Express backend, Python-based ML services, MySQL database management, and third-party APIs for notifications and geolocation. While the implementation is in progress, the expected outcome of this work is a scalable platform that can reduce food spoilage, enhance farmer income, strengthen producer–processor linkages, and build an online support network for sustainable post-harvest management. Future extensions include a mobile application with offline features, market price forecasts, and AI-driven pest/disease detection. This paper proposes the conceptual design and methodology as an ongoing research effort aimed at integrating AI, digital tools, and community support for sustainable agriculture.