Explore neural simulation-based inference techniques in this comprehensive lecture, part of the "Statistical Methods and Machine Learning in High Energy Physics" program. Delve into advanced topics presented by experts Elham E Khoda and Aishik Ghosh from the International Centre for Theoretical Sciences. Gain insights into the application of deep machine learning in high energy physics research, particularly in analyzing large datasets from experiments like the Large Hadron Collider. Learn about classification, identification, characterization, and estimation strategies used in searches for new physics. Benefit from this lecture's contribution to human resources development and capacity building in deep machine learning and artificial intelligence frameworks for high energy physics.