technologies can provide advice, new findings, and advanced approaches to solving the problems of transforming conventional systems into smart and sustainable networks. In this context, we are constantly on the lookout for state-of-the-art high-quality papers based on ML, data mining, and IoT applications. We applied a rigorous peer-review process and selected published work from 50 submitted papers, which we briefly summarize here. In this Special Issue, the use of ML methods for smart and sustainable systems is addressed by several articles. In Contribution 1, the authors demonstrate ML models using explainable artificial intelligence (XAI) for the detection of malicious domains. Therein, 45,000 samples were used with various interpretable ML and black box ensemble models. The authors claim 98% accuracy in detecting benign and malicious domains. In Contribution 2, the authors used ML-based feature selection to improve the electricity load forecasting of the smart grid. The assembly of an integration model along with a binary genetic algorithm is proposed to optimize the mean absolute percentage error. The method is potentially helpful in improving accuracy in the load scheduling of decentralized, small-scale generation units. In Contribution 3, the authors present a review of the use ML algorithms for supervisory control and data acquisition in intrusion detection and classification systems. In this article, a compression of various supervised learning methods regarding datasets, feature engineering, methodologies, and optimization mechanisms and classification procedures is considered, which can help in the design of purpose-built security systems. In Contributions 4 and 5, we can find the uses of ML with some interesting applications related to the smart education and energy sectors. In Contribution 6, the authors proposed an efficient multimedia content delivery architecture for a better quality of experience (QoE) and smooth operation of the Internet protocol. The authors claim that the proposed design improves the QoE of 4G/LTE users for multimedia data such as distribution, streaming, and banking operations. In Contribution 7, the author analyzed the relationship between the sustainable use and service quality of AI-based voice search technology through a user survey in Korea. The author found that the voice search service has a positive effect on the user's interactivity in terms of sustainable use, playfulness, certainty, and empathy. This study has the potential to advance the technology used in the design of voice search services, where direct interaction with users is not possible. In Contribution 8, the authors proposed a facial and emotion detection system using deep-learning techniques and contrast-enhancement image procedures. The authors claim that the proposed system carries 99% accuracy regarding the detection of drivers' emotions through their real-time facial images. In Contribution 9, the authors proposed a satellite-based object detection framework, which uses SAR images received from a space-based remote sensing installation. This framework operates through solar energy in a lightweight IoT environment with less computational complexity, low power and high precision, so it can help to design large-scale, SAR-image-based, echo-friendly satellite constellations. In Contribution 10, the authors proposed an authentication method using an electrocardiogram (ECG) to protect against forgery attacks. This method uses a binarized ECG signal through the feature-extraction method using artificial neural networks (ANN) to improve the authentication process. The authors report that their proposed system improved the accuracy to 98%, while the error rate is reduced to 2% compared to prior systems. In Contribution 11, another lightweight cryptographic authentication scheme is proposed for a wireless body area network.