Abstract
Object identification and classification are extremely important in all industries, whether it's healthcare, manufacturing, or environmental science. The traditional ways of doing it are pretty slow and require specific expertise. However, with the emergence of AI, ML, and DL, object identification is becoming faster and much more accurate. This review focuses on the current tech for object identification by considering CNNs, ensemble learning, and other DL models.

Introduction
Object identification is a great application in quality control, environmental monitoring, and medical diagnostics. Manual identification is time-consuming and prone to errors. AI transformed the process by automating object recognition through image processing and deep learning. This paper discusses the various technologies as compared to their methods, accuracy, and practical applications.

Machine Learning Approaches

There have been vast applications of ML towards object detection and classification. Such methodologies start through extraction of image features, such as colors, textures, and shapes and apply different algorithms for classification, Support Vector Machines and Random Forests, where in majority instances; an accuracy level of 85 to 93 percent is achieved. Although ML models may produce sound outcomes, they rely much on hand feature extraction. This can scale its ability across different datasets.

DL Approaches

DL model CNNs is revolutionizing object identification. It eliminates the need for manual feature extraction and improves accuracy. Since CNNs can handle very large datasets and high-resolution images, it will be best suited for even the most complex object recognition tasks.
Deep CNNs, usually combined with GAP, have achieved accuracy of over 99%. Such models are particularly good for applications where high accuracy is critical, such as medical imaging and defect detection. However, the price is that they consume much more computational resources and require larger datasets to train.

MobileNet is a lightweight CNN that balances performance and efficiency. It is designed to perform object identification in real-time and has achieved 98.3% accuracy, thus aligning well with mobile and cloud platforms. It is quite suitable for edge devices and on-the-go applications but needs regular internet access for cloud processing.

Ensemble Learning Techniques

Ensemble learning involves the combination of multiple models for improved overall performance. It increases accuracy up to about 97-99% as it is less prone to overfitting and helps fill the gap for weaker models. In the ensemble method, a number of different models are integrated, so there is strong performance with respect to different datasets. The deployment and management of an ensemble model, however, is challenging, which requires more coordination and computing power.

Applications in Object Detection

DL is not only used for object ID; it has also been largely used for defect detection, medical imaging, and surveillance. CNNs and DBNs have found great applications in the recognition and classification of objects in different environments. The techniques of XAI are also being used to understand how such models make decisions.

Comparative Analysis
The traditional ML approaches include SVM and RF. Those typically yield more than 85-93% accuracy, work for most relatively trivial tasks, and demand quite labor-intensive engineering of features; otherwise, their applications would be confined.CNN-based DL approaches extend to pushing the accuracy into 95-99%. With fully automatic feature extraction, a large, diversified set might be handled; it's a highly versatile tool, however very computationally expensive. Ensemble learning offers something of the middle ground: a combination of several models for an accuracy between 97 to 99%. While working efficiently, it also introduces model management issues in the deployment. For real-time cases, MobileNet delivers very good performance with an accuracy value of 98.3%. Though being very lightweight for mobility and cloud-based systems, this does have some inherent flaws: internet dependency within such areas may be problematic.

Conclusion
DL, especially CNNs, has taken object identification and detection to a whole new level of precision and efficiency over traditional ML. Ensemble learning improves the reliability, but issues such as data availability and computing power remain. Future research would include lightweight DL models, larger datasets, and enhancing interpretability with explainable AI.