Smart irrigation prediction uses Deep Learning (DL) and weather data to forecast exact watering needs, helping farms save water, cut costs, soil condition and improve crop health. Therefore, in this paper, a hybrid Vision Transformer based Temporal Convolutional Network (ViT-TCN) with Sparrow Search Optimization (SSA) is proposed for smart irrigation prediction using environmental and soil condition analysis. Data visualization transforms complex sensor and prediction data into clear, interactive dashboards that help operators to monitor field health and optimize watering schedules in real time. The integration of a ViT-TCN framework effectively reduces the challenge of precision agriculture by processing both complex environmental imagery and continuous time-series data simultaneously. The SSA improves smart irrigation prediction by smart feature selector and hyperparameter tuner. The performance analysis is carried out using python software and assesses the model's performance using key metrics such as confusion matrix, and ROC-AUC. The proposed ViT-TCN-SSA uses three classes, they are low irrigation, medium irrigation and high irrigation. The proposed ViT-TCN-SSA achieves high accuracy of 98% and better convergence speed. The proposed method outperforms conventional techniques, contributing a robust and reliable tool for smart irrigation prediction using environmental and soil condition analysis.