The recent advancements in the field of Scientific Machine Learning (SciML) are significantly enhancing predictive capabilities across various scientific domains. A notable trend is the integration of domain-specific knowledge with neural networks, leading to more interpretable and scientifically grounded models. This approach is particularly impactful in fields like battery degradation prediction in electric vehicles, where the combination of data-driven insights and SciML's interpretability is crucial for robust battery management and sustainability. Additionally, the application of SciML frameworks such as Neural ODEs and Universal Differential Equations to complex astronomical equations, such as the Chandrasekhar White Dwarf Equation, is opening new avenues for forecasting in scientific domains. These models not only improve prediction accuracy but also introduce novel metrics like the forecasting breakdown point, which is critical for understanding model limitations. Furthermore, the use of deep learning and transfer learning techniques for predicting thermophysical properties of molten salt mixtures and estimating photometric metallicity of stars from large astronomical datasets is demonstrating the versatility and power of advanced computational methods in handling big data challenges. These developments collectively underscore the transformative potential of SciML in advancing scientific understanding and precision across diverse fields.