In the contemporary landscape, the agricultural sector has undergone transformational changes, embracing progressive dynamics through the integration of cutting-edge technologies and methodologies. The global adoption of IoT, AI/ML, and similar innovations reflects their capacity to furnish precise, reliable, and consistent outcomes, enabling data-driven decision-making. The amalgamation of IoT and ML models emerges as a strategic choice to enhance data analytics, aiming at a substantial improvement in both the quantity and quality of agricultural yields. This paper seeks to elucidate the synergistic interaction of various agrarian elements, orchestrating the computational prowess facilitated by ML models and data derived from IoT sensors transmitted through LoRa-based Wireless Sensor Network (WSN) infrastructure. The proposed system advocates a comprehensive three-tier architectural framework, wherein ML and IoT systems collaborate seamlessly to process data for field management. The envisioned system extends its utility by translating these predictions into visually comprehensible representations. The integrated approach signifies a departure from conventional methods, offering a novel pathway to ensure the sustainability of yield, encompassing both production and grading standards. This paper thus contributes to the discourse on leveraging advanced technologies for agricultural optimization in response to evolving global demands.