One of the approach could be to use pattern matching like below
(Assumption - Each query is separated by newline and one complete sql query is in one line)
library(stringr)
library(dplyr)
library(tidyr)
#read file having sql query 
txt <- readLines("test.txt")
#extract column name & table name
df <- data.frame(column_name = str_match(txt, "Select\\s+(.*?)\\s+from")[,2],
                 table_name  = str_match(txt, "from\\s+(.*?)\\s+where")[,2])
#clean above extracted data to have the final outcome
df <- df %>%
  separate_rows(column_name, sep = ",") %>%
  separate_rows(table_name, sep = ",") %>%
  filter(word(trimws(column_name), 1, sep = "\\.") == word(trimws(table_name), -1)) %>%
  mutate(column_name = word(trimws(column_name), -1, sep = "\\."),
         table_name  = word(trimws(table_name), 1))
which gives
> df
  column_name table_name
1        Name   employee
2         Age   employee
3   Dept_Name department
Sample data:
test.txt contains
Select T1.Name , T1.Age, T2.Dept_Name from employee T1 , department T2 where T1.Dept_No= T2.Dept_No