You don't say what kind of gene IDs you have. If you have, or can convert to KEGG gene IDs (such as eco:b0002), genes -> pathways is easy using the TogoWS REST service.

curl http://togows.dbcls.jp/entry/genes/eco:b0002/pathways.json
[
  {
    "eco00260": "Glycine, serine and threonine metabolism",
    "eco00270": "Cysteine and methionine metabolism",
    "eco00300": "Lysine biosynthesis",
    "eco01100": "Metabolic pathways",
    "eco01110": "Biosynthesis of secondary metabolites",
    "eco01120": "Microbial metabolism in diverse environments",
    "eco01230": "Biosynthesis of amino acids"
  }
]

Here's a cleaner version that assumes you've cleaned up the header and footer from the REST output.

Also, hopefully, this example is expressive enough so you can tweak it for future use.

import sys
#
# Handle i/o filenames
#
input_fname = sys.argv[1]
output_fname = sys.argv[2]
#
# Process data
#
data_dict = {}
for line in file(input_fname):
    line = line.strip()
    spl = map(lambda x: x.strip(), line.split() ) # Default split by space, clean extra whitespace(s)
    pathway = spl[0] # Pathway name
    genes = spl[1:] # List of genes
    for gene in genes:
        current_pathways = data_dict.get(gene, []) # Obtain existing list of pathways associated with gene
        current_pathways.append(pathway) # Append to existing list of pathways, if the list exists
        current_pathways = list(set(current_pathways)) # Make pathway list unique
        data_dict[gene] = current_pathways # Let's update the dictionary
#
# Flush output to file
#
ofile = file(output_fname, "w")
for gene, pathway_list in data_dict.iteritems():
    pathways = " ".join(pathway_list) # Space separated list of pathways, or your choice of delimiter
    ofile.write("%(gene)s\t%(pathways)s\n" % dict(pathways=pathways, gene=gene)) 
ofile.close()

#!/use/bin/env python  
import sys  
paths={}
for l in sys.stdin:
    p=l.rstrip().split()
    for i in range(1,len(p)):
        if p[i] not in paths:paths[p[i]]=[]
        paths[p[i]].append(p[0])
for k in paths:
    print(k,'\t'.join(paths[k]),sep='\t')

I didn't bebug the above script, but I think it would work. I know there might be some indent issues if you just copy the python script from Biostars.

usage:

cat input | python thisscript.py

I tried it using Perl Hashes of arrays.

    open FH,"input.txt";
    %HoA=();
    print "___________DATA_________\n";
    while(<FH>)
    {
    print "$_";
        @array=split;
        for($i=1;$i<=$#array;$i++)
        {
        push @{ $HoA{$array[$i]} }, "$array[0]";
        }
    }
    print "___________RESULT_________\n";
    foreach $k (sort keys %HoA)
    {
        print "$k\t@{$HoA{$k}}\n";
   }

OUTPUT

    ________DATA_________
        path1   gene1 gene2
        path2   gene3 gene4 gene5 gene6
        path3   gene7 gene8 gene9
        path4   gene1
 ___________RESULT_________
        gene1    path1 path4
        gene2    path1
        gene3    path2
        gene4    path2
        gene5    path2
        gene6    path2
        gene7    path3
        gene8    path3
        gene9    path3

Another option: the R/Bioconductor package KEGGREST. See the documentation for some examples.

It looks like this one allows multiple gene queries:

library("KEGGREST")
keggLink("pathway", c("eco:b0002", "eco:b0003"))

#        eco:b0002       eco:b0002       eco:b0002       eco:b0002       eco:b0002 
# "path:eco00260" "path:eco00270" "path:eco00300" "path:eco01100" "path:eco01110" 
#       eco:b0002       eco:b0002       eco:b0003       eco:b0003       eco:b0003 
# "path:eco01120" "path:eco01230" "path:eco00260" "path:eco01100" "path:eco01120" 
#       eco:b0003 
# "path:eco01230"