IoT has applied in the field of agriculture for monitoring the agriculture field, water management, equipment and machines routine operations, and soil monitoring system. With the use of inexpensive sensors, smart irrigation aims to improve the effectiveness and sustainability of agriculture. These comprise airflow, location, optical, and mechanical sensors. Along with the real-time monitoring, identification, and categorization of objects, these sensors can be used to gather information about the position of crops and assess the condition of the soil. On the other hand, due to climatic diversification, climatic changes, soil erosion, decrease in soil fertility, it has these challenges which affect the crop productivity. Factors like soil, geographic region, and climatic parameters impact significantly on crop yield and productivity. Water scarcity for the agriculture industry is a major challenge in today’s world. The sector is witnessing transformations with the adoption of modern technologies, precision farming, and sustainable practices. This proposed paper mainly focused on attaining smart irrigation for water management and soil monitoring based on enhanced convolution neural networks with IoT. In this work, two kinds of datasets are used. Using the crop prediction dataset, a typical farmer can predict the required plant from the soil nutrients, temperature, and moisture details available in the dataset. Using the irrigation system dataset, the farmer can avail the automated drip irrigation system. Sustainable agriculture environment must account for a potential reduction in rural population while aiming to save water.