Smart farming on modern agriculture is focusing on its potential to revolutionize traditional farming through advanced technologies. Various smart farming practices, including precision agriculture, crop monitoring, pest control, and predictive analytics, are explored. The use of technologies such as autonomous machinery, IoT-based monitoring systems, and decision-support tools is discussed, emphasizing their ability to improve productivity and sustainability. The present research work is based on the concept of smart farming, defining its core principles, and discussing its critical role in addressing global challenges such as food security, resource optimization, and climate resilience. It also explores the application of artificial intelligence (AI) in agriculture, highlighting key techniques and their relevance to agricultural innovation. The chapter further presents the implementation of Random Forest Regression model to predict crop yields based on environmental and soil nutrient factors, including temperature, humidity, phosphorus, potassium, pH, and rainfall. This model, trained on crop type data, offers potential for future predictions and contributes to precision agriculture by optimizing farming practices. By providing actionable insights on crop management, irrigation, fertilization, and harvesting, the model helps guide farmers toward more efficient and sustainable decisions. This research work demonstrates the potential of smart farming, integrating AI with agricultural needs to foster a more efficient, sustainable agricultural future and maybe utilized by farmers for better yield production.