Introduction: All are interested in the estimation of the sale price of crop yield beforehand. Crop yield is dependent on the growth rate of the plant. Plant growth rate depends on factors like soil, water, sunlight, and season. Due to this multi-factor dependency, it is not easy to estimate the sale price of the crop yield or predict the timeline beforehand. Objective: By advancing AI technology, we can leverage and eliminate the challenges and predict crop yield, timeline, and sale price. We use computer vision, Deep Learning, and regression to predict the estimation of the sales price of the crop yield. Materials/Methods: By using computer vision YOLO algorithms, we detect plants in the field and categorize the plants using the CNN classification algorithm. We use IOT devices to monitor the growth of the plant from time to time and collect the data. The collected data are used to predict time, crop yield, and sale price beforehand. The prediction is derived based on historical data of sales prices in different sessions and a plant growth data set using regression algorithms. Result: The experimental results demonstrate the effectiveness of the proposed approach, where we detect the plant using computer vision; categorize the plant using CNN; and accurately predict yield, timeline, and sale price using regression. To evaluate the proposed framework, we conducted experiments using sample data. Through hypothesis testing using the "T Test" and "chi-square" test, we failed to reject the null hypothesis, and the evaluation metrics show that the accuracy of plant detection is 92.5; the categorization of plants using CNN is 96.31; and the accuracy score obtained using regression to predict yield, timeline, and sale price is 91.57. The Precision, Recall, and F1 scores also look good.