How would I debug this to write webscraped content to CSV under field names? (Python, BeautifulSoup)

One problem with your approach is that if an element is missing, all the lists will be out of sync when you try and write them. A better approach is to find an HTML element that encompasses one result and then just get the elements for just the one entry. This can then be written a row at time.

As some items are not always present, you can use a function to return a blank string for when the .find() failed to locate a suitable value.

For example:

from bs4 import BeautifulSoup
import csv, requests, re


def get_text(soup):
    ''' Return the text if available else an empty string '''
    return soup.get_text(strip=True) if soup else ''
    
    
# The following code is used to set the pythonssl default cipher list - needed if the server has an outdated openssl
try:
    requests.packages.urllib3.contrib.pyopenssl.DEFAULT_SSL_CIPHER_LIST += 'HIGH:!DH:!aNULL'
except AttributeError:
    # no pyopenssl support used / needed / available
    pass
requests.packages.urllib3.util.ssl_.DEFAULT_CIPHERS += 'HIGH:!DH:!aNULL'

# Set HTML headers to emulate Firefox browser to avoid bot security
# Potentially unused, but good practice to enable proper script function
html_headers = {    
    'Access-Control-Allow-Origin': '*',
    'Access-Control-Allow-Methods': 'GET',
    'Access-Control-Allow-Headers': 'Content-Type',
    'Access-Control-Max-Age': '3600',
    'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:52.0) Gecko/20100101 Firefox/52.0'
    }


# Download BMO branch entries
base_url = "https://www.yellowpages.ca/search/si/{}/Bmo+Banque+De+Montr%C3%A9al/Canada" # URL structure of entries
first_page_request = requests.get(base_url.format("1"), headers=html_headers)   # Format method used to insert page number into greater URL
first_page_content = BeautifulSoup(first_page_request.content, "html.parser")   # Grabs all page content using Beautiful Soup module and standard Python parser

# Get total HTML page count for BMO entries
page_count_class = first_page_content.find_all(class_="pageCount")[0] 
total_pages = re.search("([0-9]+)</span>", str(page_count_class)).group(1)  # Extract the last page number from the total page count at the bottom of the page

# Initial CSV handling

with open("bmo_data.csv", "w", encoding="latin-1", newline="") as f_output:   # Chose w mode to write to a file
    csv_output = csv.writer(f_output)   # Setting the writer
    csv_output.writerow(["name", "street_address", "city", "province", "postal_code"])

    # Running iterative loop through each page of the yellowpages listings for BMO
    for page in range(1, int(total_pages) + 1):
        print(f"Getting page {page} of {int(total_pages)}")
        page_request = requests.get(base_url.format(page), headers=html_headers)
        page_html_content = BeautifulSoup(page_request.content, "html.parser")

        for div in page_html_content.find_all('div', class_="listing_right_section"):
            entry_name = div.find(class_="jsListingName")    # Get store name
            entry_address = div.find("span", itemprop="streetAddress")   # Get street address HTML object
            entry_city = div.find("span", itemprop="addressLocality")   # Get city HTML object
            entry_province = div.find("span", itemprop="addressRegion")  # Get province HTML object
            entry_postal = div.find("span", itemprop="postalCode")    # Get postal code HTML object
            
            csv_output.writerow([
                get_text(entry_name), 
                get_text(entry_address), 
                get_text(entry_city), 
                get_text(entry_province), 
                get_text(entry_postal)])

If the fieldnames are fixed, you don't need to use DictWriter(), that is up to you though.

This would give output starting:

name,street_address,city,province,postal_code
BMO Bank of Montreal,270 Dundas St,London,ON,N6A 1H3
BMO Banque De Montréal,51 Notre Dame E Victorvl,Victoriaville,QC,G6P 1R6
BMO Banque De Montréal,739 Rue Conseil Sher,,QC,