Can I do the below problem with ML? My dataset has 3 fields ColumnX, ColumnA, Column B as below.

Column X, Column_A ,      Column_B

AA, 1     ,        KEYWORD1 KEYWORD2 KEYWORD3 

AA, 2    ,         KEYWORD4 KEYWORD5 KEYWORD6

XX, 3 , KEYWORD3 KEYWORD7

XX, 4, KEYWORD6 KEYWORD8

YY, 5, KEYWORD9

I removed the stop words in Column_B and kept the keywords.

So here Can I train a model which can classify the data with Column_X and Column_B and given an input from user which matches any keyword in Column B , returns Column_A

Sample output:

user gives an input : KEYWORD9.

my output should show ColumnX: YY and ColumnA: 5

I know it can be done with basic python but I want to use ML here
Try an LSTM Encoder-decoder model. The encoder takes Column_B as input and Decoder gives Column X, Column_A as output. Or create a two machine learning models one for each column prediction.