While XML as a data format can take many forms from flat to deeply nested, data frames must adhere to a single structure of two dimensions: row by column. Hence, as noted in docs, pandas.read_xml, is a convenience method best for flatter, shallow XML files. You can use xpath to traverse different areas of the document, not just the default /*.

However, you can use XSLT 1.0 (special purpose language designed to transform XML files) with the default parser, lxml, to transform any XML to the needed flat format of data frame. Below stylesheet will restyle the <slike> node for comma-separated text of its children <slika>:

XSLT (save as .xsl file, a special .xml file)

<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">
    <xsl:output method="xml" omit-xml-declaration="no" indent="yes"/>
    <xsl:strip-space elements="*"/>

    <xsl:template match="node()|@*">
     <xsl:copy>
       <xsl:apply-templates select="node()|@*"/>
     </xsl:copy>
    </xsl:template>
    
    <xsl:template match="slike">
     <xsl:copy>
       <xsl:for-each select="*">
         <xsl:value-of select="text()"/>
         <xsl:if test="position() != last()">
            <xsl:text>,</xsl:text>
         </xsl:if>
       </xsl:for-each>
     </xsl:copy>
    </xsl:template>  
</xsl:stylesheet>

Online Demo

Python

artikal_df = pd.read_xml("my_filename.xml", stylesheet="my_style.xsl") 

# CONVERT COMMA-SEPARATED VALUES TO EMBEDDED LISTS
artikal_df["slike"] = artikal_df["slike"].str.split(',')

# PREFIX PARENT NODE NAME
artikal_df = artikal_df.add_prefix('artikal_')

artikal_df.info()
# <class 'pandas.core.frame.DataFrame'>
# RangeIndex: 2 entries, 0 to 1
# Data columns (total 12 columns):
#  #   Column               Non-Null Count  Dtype  
# ---  ------               --------------  -----  
#  0   artikal_id           2 non-null      int64  
#  1   artikal_sifra        2 non-null      int64  
#  2   artikal_barKod       2 non-null      int64  
#  3   artikal_naziv        2 non-null      object 
#  4   artikal_kategorija1  2 non-null      object 
#  5   artikal_kategorija2  2 non-null      object 
#  6   artikal_kategorija3  2 non-null      object 
#  7   artikal_vpCena       2 non-null      float64
#  8   artikal_mpCena       2 non-null      float64
#  9   artikal_dostupan     2 non-null      int64  
#  10  artikal_opis         0 non-null      float64
#  11  artikal_slike        2 non-null      object 
# dtypes: float64(3), int64(4), object(5)
# memory usage: 320.0+ bytes

You can use * as wildcard in xpath and // to dive deeper:

import pandas as pd

xml="""your xml here ..."""

df = pd.read_xml(xml, xpath= './/*')
print(df)