Fruit quality evaluation is becoming more and more important in today’s society since it is crucial to the food and agricultural sectors. Fruit flaws must be found to preserve quality. Utilizing cutting-edge technology is crucial for the agri-food sector. The capacity of deep learning (DL) and machine learning (ML) to extract strong features from datasets has led to their widespread use in a variety of industries. Fruit species vary in shape, color, and texture, making it difficult to identify them and estimate their production. The physical counting of fruits at different stages of growth is a labor-intensive and costly process for estimating crop production. These problems spur the creation of an intelligent system for detecting fruit quality and estimating production, which will help farmers make better decisions about harvesting, marketing, etc. To achieve long-term economic growth with minimal resource depletion, “smart farming” employs state-of-the-art technology to integrate intelligent systems across the whole agricultural sector. State-of-the-art outcomes in smart agricultural applications are provided by ML and DL. Additionally, we explore deep learning methodologies such as convolutional neural networks (CNNs). This work addresses open research concerns and challenges and evaluates the literature utilizing multiple strategies for ML and DL model-based fruit quality detection and yield estimate.