diff --git a/bayes_opt/logger.py b/bayes_opt/logger.py
index 69f40d19..7198febf 100644
--- a/bayes_opt/logger.py
+++ b/bayes_opt/logger.py
@@ -271,7 +271,18 @@ def log_optimization_step(
         result : dict[str, Any]
             The result dictionary for the most recent step.
 
-        params_config : Mapping[str, ParamsType]
+            # Read current data
+            with self._path.open("r") as f:
+                fileData = json.load(f)
+
+            # Append next data point
+            fileData.append(data)
+
+            # Writes content back to a file
+            with self._path.open("w") as f:
+                json.dumps(fileData)
+
+        params_config: Mapping[str, ParamsType]
             The configuration to map the key to the parameter for correct formatting.
 
         current_max : dict[str, Any] | None
diff --git a/bayes_opt/util.py b/bayes_opt/util.py
index ea68889f..5a7edf63 100644
--- a/bayes_opt/util.py
+++ b/bayes_opt/util.py
@@ -2,8 +2,58 @@
 
 from __future__ import annotations
 
+import json
+from collections.abc import Iterable
+from os import PathLike
+from pathlib import Path
+
 import numpy as np
 
+# from bayes_opt.bayesian_optimization import BayesianOptimization
+
+
+def load_logs(
+    optimizer: BayesianOptimization, logs: str | PathLike[str] | Iterable[str | PathLike[str]]
+) -> BayesianOptimization:
+    """Load previous ...
+
+    Parameters
+    ----------
+    optimizer : BayesianOptimizer
+        Optimizer the register the previous observations with.
+
+    logs : str or os.PathLike
+        File to load the logs from.
+
+    Returns
+    -------
+    The optimizer with the state loaded.
+
+    """
+    if isinstance(logs, (str, PathLike)):
+        logs = [logs]
+
+    for log in logs:
+        try:
+            with Path(log).open("r") as fil:
+                fileData = json.load(fil)
+        except json.JSONDecodeError:
+            print(f"ERROR: JSON decode error when decoding '{log}'")
+            continue
+
+        for iteration in fileData:
+            # Prevents duplicate points being registered when an exception can be raised
+            if not optimizer._allow_duplicate_points and iteration["params"] in optimizer:
+                continue
+
+            optimizer.register(
+                params=iteration["params"],
+                target=iteration["target"],
+                constraint_value=(iteration["constraint"] if optimizer.is_constrained else None),
+            )
+
+    return optimizer
+
 
 def ensure_rng(random_state: int | np.random.RandomState | None = None) -> np.random.RandomState:
     """Create a random number generator based on an optional seed.