How to obtain the final values of a DecisionTreeRegressor in Scikit-Learn?
For documentation, check the Scikit-Learn code at Github i.e. line#535 [Link]

value : array of double, shape [node_count, n_outputs, max_n_classes]
Contains the constant prediction value of each node.

You can check the same using this sample code.

Identify the leaf nodes
Slice the value attributes for leaf nodes

from sklearn.datasets import load_diabetes
import numpy as np, matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeRegressor

X, y = load_diabetes(return_X_y=True)
regressor = DecisionTreeRegressor(max_depth=5)
regressor.fit(X, y)

children_left = regressor.tree_.children_left
children_right = regressor.tree_.children_right
leaf_nodes = []
for i in range(n_nodes):
    if children_left[i] == children_right[i]:
        leaf_nodes.append(i)

y_pred =  regressor.predict(X)
all_val = regressor.tree_.value[leaf_nodes,0,0] # Sliced on Leaf nodes

# Check if all the y_pred is from these values
set(y_pred) - set(all_val) # Or, [elem for elem in y_pred if elem not in all_val]