Using pandas and BeautifulSoup you can achieve your expected output easily:

#Code:

import pandas as pd
import itertools
from bs4 import BeautifulSoup as b
with open("file.xml", "r") as f: # opening xml file
    content = f.read()

soup = b(content, "lxml")
pkgeid =  [ values.text for values in soup.findAll("pkgeid")]
pkgname = [ values.text for values in soup.findAll("pkgname")]
time =  [ values.text for values in soup.findAll("time")]
oper =  [ values.text for values in soup.findAll("oper")]
# For python-3.x use `zip_longest` method
# For python-2.x use 'izip_longest method
data = [item for item in itertools.zip_longest(time, oper, pkgeid, pkgname)] 
df  = pd.DataFrame(data=data)
df.to_csv("sample.csv",index=False, header=None)

#output in `sample.csv` file will be as follows:
2015-09-16T04:13:20Z,Create_Product,10,BBCWRL
2015-09-16T04:13:20Z,Create_Product,18,CNNINT
2018-04-01T03:30:28Z,Deactivate_Dhct,,

Consider reading URL without requests or escaping content directly into pandas.read_xml(). Per docs, emphasis added:

path_or_buffer: str, path object, or file-like object

String, path object (implementing os.PathLike[str]), or file-like object implementing a read() function. The string can be any valid XML string or a path. The string can further be a URL. Valid URL schemes include http, ftp, s3, and file.

import pandas as pd

url = (
    "http://90.161.233.78:65519/services/user/records.xml?"
    "begin=04052022?end=06052022?var=EDSLINEEMBEDDED.Module2.VI1?"
    "var=EDSLINEEMBEDDED.Module2.API1?period=900"
)

df = pd.read_xml(url, xpath="//record/* | //dateTime")

# FILL PARENT TEXT FORWARD TO CHILD ITEMS
df["dateTime"] = df["dateTime"].ffill()

# DROP UNNEEDED ROWS
df[(pd.notnull(df["id"])) & (pd.notnull(df["value"]))]

df.to_csv('output0.csv')