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Artificial Intelligence’s impact on decision-making is growing, but understanding is limited. Legislation is needed to understand AI’s effects and improve human-centric design. Generative Artificial Intelligence (GenAI) uses large database patterns to generate new data instances, while Explainable Artificial Intelligence (XAI) allows human users to understand and trust the results of machine learning algorithms, providing a platform for conference attendees. The ICAAAI-2024 conference will focus on generative adversarial networks (GANs), variational autoencoders (VAEs), and transformers for natural language processing tasks. Large language models (LLMs) are used for language generation, classification, and more. Both fields are crucial for AI advancement, addressing challenges in creative fields and regulated industries. Generative AI is revolutionizing academia and industry 6.0 by enabling machines to participate in creative processes. Explainable AI is essential for trust and accountability, requiring interdisciplinary approaches in mathematical modeling and statistical AI. Techniques like SHAP, LIME, and ALE are used to provide global and local explanations of a model’s operating rules. Statistical AI models like linear regression, logistic regression, decision trees, SVMs, Naive Bayes, KNN, and neural networks are used for various tasks. AI models are made simpler with explainable algorithms such as LIME, SHAP, and SBRL, which also improve user experience and cut down on errors. There’s been an increase in calls for operational openness as AI becomes more commonplace in daily life. However, poor societal outcomes from AI conclusions can include prejudice against disabled communities and erroneous apprehensions. To follow the guidelines of Information Communication Technology (ICT), which focuses on improving data generation, handling, and exchange methods by infrastructure and tools for Advanced Techniques of ICAAAI-2024. Algorithms in Advanced Artificial Intelligence is a collection of papers on emerging issues, challenges, and new methods in Artificial Intelligence, Machine Learning, Deep Learning, Cloud Computing, Federated Learning, Internet of Things, and Blockchain technology. The book addresses the growing attention to advanced technologies due to their ability to provide “paranormal solutions” to problems associated with classical Artificial Intelligence frameworks. AI is used in various subfields, including learning, perception, and financial decisions. It uses four strategies: Thinking Humanly, Thinking Rationally, Acting Humanly, and Acting Rationally. The authors address various issues in ICT, including Artificial Intelligence, Machine Learning, Deep Learning, Data Science, Big Data Analytics, Vision, Internet of Things, Security and Privacy aspects in AI, and Blockchain and Digital Twin Integrated Applications in AI.