The Marathwada region of Maharashtra, India, faces chronic agricultural distress driven by severe water scarcity, recurring droughts, and rapid climate variability. Traditional smallholder farming systems in this semi-arid belt are increasingly unviable, leading to widespread economic stagnation and rural distress. This paper proposes a localized, low-cost AI-Enabled Rural Development Framework (AI-RDF) specifically engineered for the Marathwada ecosystem. The framework integrates an Internet of Things (IoT) ground-sensing layer with hybrid cloud-edge Machine Learning (ML) models. It explicitly targets three regional bottlenecks: predictive precision irrigation for water-intensive crops like sugarcane and cotton, early-stage pest and disease detection for cash crops, and localized AI-driven market-demand forecasting.
Using simulated regional soil-moisture datasets and historical weather parameters from Marathwada districts (including Chhatrapati Sambhaji Nagar, Beed, and Jalna), we validate a Long Short-Term Memory (LSTM) network for predictive evapotranspiration. Results demonstrate that the localized AI models can reduce agricultural water expenditure by 34% while optimizing crop yields by 18%. Finally, the paper outlines a socio-technical deployment roadmap designed to bypass regional digital literacy barriers, presenting a scalable blueprint for AI-mediated rural transformation across developing dryland economies.