The agricultural sector faces significant challenges due to climate change, unseasonal rains, and droughts, necessitating advanced, predictive approaches for improved farming practices. This paper introduces AgroAdvisor, a novel system developed to support farmers with real-time data and predictive analytics throughout both crop cultivation and post-harvest phases. AgroAdvisor integrates four models: the Crop Recommendation Model, which employs a random forest algorithm to achieve 99.09% accuracy in crop recommendations; the Market Demand Analysis Model, using the ARIMA model to forecast market trends with 95% accuracy; the Fertilizer Recommendation Model, which provides precise fertilizer suggestions based on sensor-collected soil data; and the Plant Disease Detection Model, utilizing ResNet for disease identification from images with 99.2% accuracy. This research addresses immediate agricultural challenges as well as sets a foundation for future precision farming innovations, enhancing sustainability, profitability, and contributing to global food security and rural development.