The agriculture industry is undergoing a great transition because of artificial intelligence (AI), which improves productivity, allocates resources optimally, and addresses urgent issues like food security, workforce shortages, and climate change. Artificial intelligence (AI)-driven technologies, including computer vision, robots, machine learning, and the Internet of Things (IoT), are transforming traditional farming practices into precision-based, data-driven systems. These developments save operating costs, increase overall production, and empower farmers to make better decisions. The integration of AI in agriculture has led to various innovative applications, such as continuous monitoring of crop, prediction for yield forecasting, automated irrigation systems, early detection of pests and plant diseases, and intelligent farm management. Additionally, AI-powered drones and autonomous agricultural machinery are further streamlining different operations of farming, minimizing the dependency on labor while maximizing efficiency and yield. Despite having the potential to revolutionize agriculture, a number of obstacles still exist in AI. Issues like data security, high implementation costs, and the requirement for skilled professionals present obstacles to widespread adoption. Overcoming these barriers necessitates collaborative efforts from researchers, industry experts, and policymakers to develop accessible and cost-effective AI solutions. This study intends to investigate the most recent uses of AI in agriculture, examine the difficulties in putting them into implementation, and propose innovative solutions to facilitate the seamless integration of AI technologies into modern farming practices.