how to include minority class in all folds I am working on an imbalance dataset with a 98:2 ratio (1M record in the majority class and 20K in the minority class)
I am planning to run my model for 30 folds, 

*

*I tried with stratified K folds but here again, each fold will have an imbalance problem again

*I don't want to use SMOTE analysis as it creates data loss or Overfitting

which cross-validation techniques or hyperparameters should I use so that all Minority classes are included in each fold?

I think including minority class in all folds and answering imbalance problem are two separate issues.
If you use StratifiedKFold with 30 folds, that means you will train your model on 29/30 ~ 97% of your majority class and your minority data. Your test data will be ~3% of each class. That is a good approach but you'll have to face the imbalance problem, true. As you said, SMOTE is one approach, you could also use random subselection of majority class, or the class_weight hyperparameter of some classifiers, or ensemble approaches, or other approaches...
So, combining StratifiedKFold + imbalance handling techniques seems the right approach to me.
About:

so that all Minority classes are included in each fold?

I am not sure to understand what you mean by all minority classes as you said there is only one minority class, but note that if you include all samples from minority class in each fold, you will train and test your model on the same data. You definitely don't want to do that.
