Artificial Intelligence (AI) solutions in agriculture have emerged as a promising approach to enhance crop management practices and improve yield outcomes. Agriculture is a cornerstone of society, essential for sustenance, economic growth, and tackling global hunger and malnutrition challenges. Algorithms for optimization are crucial for solving complex numerical problems in many scientific and technical domains. In local and global search, respectively. This paper elucidates the transformative impact of AI like the various algorithm and advance IoT on agricultural practices. It also evaluates need of AI in parameter of agriculture. Mainly we focused on soil monitoring using AI technology. AI plays a crucial role in modern farming by analysing real-time soil data from IoT sensors, enabling predictive insights on soil properties. This study proposes a Hybrid Ant Lion Optimizer (ALO) and Particle Swarm Optimization (PSO) approach to improve AI-based soil analysis and agricultural precision. ALO prevents premature convergence during global exploration while excelling in ALO and local exploitations are enhanced by PSO, guaranteeing accurate and faster convergence. Subsequently, the hybrid model combines these algorithms to optimize soil health assessment's key parameters like nutrient levels, moisture content, and crop use along with remote UAV/drone sensing data