The recent advancements in the integration of Artificial Intelligence (AI) and Machine Learning (ML) across various scientific and technological domains are significantly reshaping traditional methodologies. In the realm of drug discovery, AI and ML are streamlining the process, offering faster and more efficient ways to identify potential drug compounds, thereby addressing the global challenge of antimicrobial resistance. Similarly, in the field of cybersecurity, advanced chatbots powered by Large Language Models (LLMs) are revolutionizing threat intelligence delivery, providing instant and tailored responses to enhance security postures. The automation of literature reviews through collaborative knowledge minigraph agents is another notable development, significantly reducing the time and effort required for comprehensive academic assessments. Microfluidic device design is also benefiting from the integration of LLMs, enhancing the accuracy and efficiency of droplet-based microfluidic systems. Additionally, the detection of AI-generated content in scientific papers is being advanced through multi-head span-based detectors, ensuring the integrity of published research. Regulatory data analysis in the medical device sector is being automated to provide insights into the adoption and impact of AI-enabled medical devices. Lastly, the extraction of deep learning methodologies from scientific publications is being facilitated by harnessing multiple LLMs, promoting reproducibility and knowledge transfer across diverse scientific domains.