Deep learning models have been widely used in various applications, such as image and speech recognition, natural language processing, and recently, in the field of drought forecasting/prediction. These models have proven to be effective in handling large and complex datasets, and in automatically extracting relevant features for forecasting. The use of deep learning models in drought forecasting can provide more accurate and timely predictions, which are crucial for the mitigation of drought-related impacts such as crop failure, water shortages, and economic losses. This review provides information on the type of droughts and their information systems. A comparative analysis of deep learning models, related technology, and research tabulation is provided. The review has identified algorithms that are more pertinent than others in the current scenario, such as the Deep Neural Network, Multi-Layer Perceptron, Convolutional Neural Networks, and combination of hybrid models. The paper also discusses the common issues for deep learning models for drought forecasting and the current open challenges. In conclusion, deep learning models offer a powerful tool for drought forecasting, which can significantly improve our understanding of drought dynamics and our ability to predict and mitigate its impacts. However, it is important to note that the success of these models is highly dependent on the availability and quality of data, as well as the specific characteristics of the drought event.

Additive manufacturing (AM) has been gaining pace, replacing traditional manufacturing methods. Moreover, artificial intelligence and machine learning implementation has increased for further applications and advancements. This review extensively follows all the research work and the contemporary signs of progress in the directed energy deposition (DED) process. All types of DED systems, feed materials, energy sources, and shielding gases used in this process are also analyzed in detail. Implementing artificial intelligence (AI) in the DED process to make the process less human‐dependent and control the complicated aspects has been rigorously reviewed. Various AI techniques like neural networks, gradient boosted decision trees, support vector machines, and Gaussian process techniques can achieve the desired aim. These models implemented in the DED process have been trained for high‐precision products and superior quality monitoring.

Powder bed fusion (PBF) applies to various metallic materials used in the metal printing process of building a wide range of complex parts compared to other AM technologies. PBF process has several variants such as DMLS (direct metal laser sintering), EBM (electron beam melting), SHS (selective heat sintering), SLM (selective laser melting), and SLS (selective laser sintering). For PBF to reach its maximum potential, machine learning (ML) algorithms are used with suitable materials to achieve goals cost-effectively. Various applications of neural networks, including ANNs, CNNs, RNNs, and other popular techniques such as KNN, SVM, and GP were reviewed, and future challenges were discussed. Some special-purpose algorithms were listed as follows: GAN, SeDANN, SCNN, K-means, PCA, etc. This review presents the evolution, current status, challenges, and prospects of these technologies in terms of material, features, process parameters, applications, advantages, disadvantages, etc., to explain their significance and provide an in-depth understanding of the same.