This open access book introduces and explains machine learning ML algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows.These two fields ML and turbulent combustion have large body of work and knowledge on their own and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows.ampampampnbsp This is important as to the worlds total primary energy supply TPES since more than 90 of this supply is through combustion technologies and the nonnegligible effects of combustion on environment.ampampampnbsp Although alternative technologies based on renewable energies are coming up their shares for the TPES is are less than 5 currently and one needs a complete paradigm shift to replace combustion sources.ampampampnbsp Whether this is practical or not is entirely a different question and an answer to this question depends on the respondent.ampampampnbsp However a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70 even by 2070.ampampampnbsp Hence it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and ampampquotgreenerampampquot combustion systems that are friendlier to the environment can be designed.ampampampnbspThe book covers the current state of the art in these two topics and outlines the challenges involved merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges.ampampampnbsp The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish.ampampampnbsp This book is unique since there is not any book with similar coverage of topics ranging from big data analysis and machine learning algorithm to their applications