Here is a solution using the Shapely library. Shapely is often used for GIS work, but is built to be useful for computational geometry. I changed your inputs from lists to tuples.

Problem

# Given these endpoints
#line 1
A = (X, Y)
B = (X, Y)

#line 2
C = (X, Y)
D = (X, Y)

# Compute this:
point_of_intersection = (X, Y)

Solution

import shapely
from shapely.geometry import LineString, Point

line1 = LineString([A, B])
line2 = LineString([C, D])

int_pt = line1.intersection(line2)
point_of_intersection = int_pt.x, int_pt.y

print(point_of_intersection)

Using formula from: https://en.wikipedia.org/wiki/Line%E2%80%93line_intersection

 def findIntersection(x1,y1,x2,y2,x3,y3,x4,y4):
        px= ( (x1*y2-y1*x2)*(x3-x4)-(x1-x2)*(x3*y4-y3*x4) ) / ( (x1-x2)*(y3-y4)-(y1-y2)*(x3-x4) ) 
        py= ( (x1*y2-y1*x2)*(y3-y4)-(y1-y2)*(x3*y4-y3*x4) ) / ( (x1-x2)*(y3-y4)-(y1-y2)*(x3-x4) )
        return [px, py]

If your lines are multiple points instead, you can use this version.

import numpy as np
import matplotlib.pyplot as plt
"""
Sukhbinder
5 April 2017
Based on:    
"""

def _rect_inter_inner(x1,x2):
    n1=x1.shape[0]-1
    n2=x2.shape[0]-1
    X1=np.c_[x1[:-1],x1[1:]]
    X2=np.c_[x2[:-1],x2[1:]]    
    S1=np.tile(X1.min(axis=1),(n2,1)).T
    S2=np.tile(X2.max(axis=1),(n1,1))
    S3=np.tile(X1.max(axis=1),(n2,1)).T
    S4=np.tile(X2.min(axis=1),(n1,1))
    return S1,S2,S3,S4

def _rectangle_intersection_(x1,y1,x2,y2):
    S1,S2,S3,S4=_rect_inter_inner(x1,x2)
    S5,S6,S7,S8=_rect_inter_inner(y1,y2)

    C1=np.less_equal(S1,S2)
    C2=np.greater_equal(S3,S4)
    C3=np.less_equal(S5,S6)
    C4=np.greater_equal(S7,S8)

    ii,jj=np.nonzero(C1 & C2 & C3 & C4)
    return ii,jj

def intersection(x1,y1,x2,y2):
    """
INTERSECTIONS Intersections of curves.
   Computes the (x,y) locations where two curves intersect.  The curves
   can be broken with NaNs or have vertical segments.
usage:
x,y=intersection(x1,y1,x2,y2)
    Example:
    a, b = 1, 2
    phi = np.linspace(3, 10, 100)
    x1 = a*phi - b*np.sin(phi)
    y1 = a - b*np.cos(phi)
    x2=phi    
    y2=np.sin(phi)+2
    x,y=intersection(x1,y1,x2,y2)
    plt.plot(x1,y1,c='r')
    plt.plot(x2,y2,c='g')
    plt.plot(x,y,'*k')
    plt.show()
    """
    ii,jj=_rectangle_intersection_(x1,y1,x2,y2)
    n=len(ii)

    dxy1=np.diff(np.c_[x1,y1],axis=0)
    dxy2=np.diff(np.c_[x2,y2],axis=0)

    T=np.zeros((4,n))
    AA=np.zeros((4,4,n))
    AA[0:2,2,:]=-1
    AA[2:4,3,:]=-1
    AA[0::2,0,:]=dxy1[ii,:].T
    AA[1::2,1,:]=dxy2[jj,:].T

    BB=np.zeros((4,n))
    BB[0,:]=-x1[ii].ravel()
    BB[1,:]=-x2[jj].ravel()
    BB[2,:]=-y1[ii].ravel()
    BB[3,:]=-y2[jj].ravel()

    for i in range(n):
        try:
            T[:,i]=np.linalg.solve(AA[:,:,i],BB[:,i])
        except:
            T[:,i]=np.NaN


    in_range= (T[0,:] >=0) & (T[1,:] >=0) & (T[0,:] <=1) & (T[1,:] <=1)

    xy0=T[2:,in_range]
    xy0=xy0.T
    return xy0[:,0],xy0[:,1]


if __name__ == '__main__':

    # a piece of a prolate cycloid, and am going to find
    a, b = 1, 2
    phi = np.linspace(3, 10, 100)
    x1 = a*phi - b*np.sin(phi)
    y1 = a - b*np.cos(phi)

    x2=phi
    y2=np.sin(phi)+2
    x,y=intersection(x1,y1,x2,y2)
    plt.plot(x1,y1,c='r')
    plt.plot(x2,y2,c='g')
    plt.plot(x,y,'*k')
    plt.show()

The most concise solution I have found uses Sympy: https://www.geeksforgeeks.org/python-sympy-line-intersection-method/

# import sympy and Point, Line 
from sympy import Point, Line 
  
p1, p2, p3 = Point(0, 0), Point(1, 1), Point(7, 7) 
l1 = Line(p1, p2) 
  
# using intersection() method 
showIntersection = l1.intersection(p3) 
  
print(showIntersection) 

img And You can use this kode

class Nokta:
def __init__(self,x,y):
    self.x=x
    self.y=y             
class Dogru:
def __init__(self,a,b):
    self.a=a
    self.b=b        

def Kesisim(self,Dogru_b):
    x1= self.a.x
    x2=self.b.x
    x3=Dogru_b.a.x
    x4=Dogru_b.b.x
    y1= self.a.y
    y2=self.b.y
    y3=Dogru_b.a.y
    y4=Dogru_b.b.y                          
    #Notlardaki denklemleri kullandım
    pay1=((x4 - x3) * (y1 - y3) - (y4 - y3) * (x1 - x3))      
    pay2=((x2-x1) * (y1 - y3) - (y2 - y1) * (x1 - x3))
    payda=((y4 - y3) *(x2-x1)-(x4 - x3)*(y2 - y1))        

    if pay1==0 and pay2==0 and payda==0:
        print("DOĞRULAR BİRBİRİNE ÇAKIŞIKTIR")

    elif payda==0:               
        print("DOĞRULAR BİRBİRNE PARALELDİR")        
    else:                               
        ua=pay1/payda if payda else 0                   
        ub=pay2/payda  if payda else 0  
        #x ve y buldum 
        x=x1+ua*(x2-x1) 
        y=y1+ua*(y2-y1)
        print("DOĞRULAR {},{} NOKTASINDA KESİŞTİ".format(x,y))

With the scikit-spatial library you can easily do it in the following way:

import matplotlib.pyplot as plt

from skspatial.objects import Line

# Define the two lines.
line_1 = Line.from_points([3, -2], [5, 4])
line_2 = Line.from_points([-1, 0], [3, 2])

# Compute the intersection point
intersection_point = line_1.intersect_line(line_2)

# Plot
_, ax = plt.subplots()
line_1.plot_2d(ax, t_1=-2, t_2=3, c="k")
line_2.plot_2d(ax, t_1=-2, t_2=3, c="k")
intersection_point.plot_2d(ax, c="r", s=100)
grid = ax.grid()

there is already an answer that uses formula from Wikipedia but that doesn't have any check point to check if line segments actually intersect so here you go

def line_intersection(a, b, c, d):
    t = ((a[0] - c[0]) * (c[1] - d[1]) - (a[1] - c[1]) * (c[0] - d[0])) / ((a[0] - b[0]) * (c[1] - d[1]) - (a[1] - b[1]) * (c[0] - d[0]))
    u = ((a[0] - c[0]) * (a[1] - b[1]) - (a[1] - c[1]) * (a[0] - b[0])) / ((a[0] - b[0]) * (c[1] - d[1]) - (a[1] - b[1]) * (c[0] - d[0]))

    # check if line actually intersect
    if (0 <= t and t <= 1 and 0 <= u and u <= 1):
        return [a[0] + t * (b[0] - a[0]), a[1] + t * (b[1] - a[1])]
    else: 
        return False
    
#usage
print(line_intersection([0,0], [10, 10], [0, 10], [10,0]))

#result [5.0, 5.0]