Can we predict when a Google Update will land?

Google algorithm updates often feel unpredictable, but is there a pattern behind when they happen.

I wanted to know if there’s a pattern behind when they happen, and what kind of update they’re likely to be.

So, I reviewed five years of official Search Status data (2020–2025) and combined it with articles covering "all" official Google updates from as far back as 2020 - so there's roughly 25 years of data going into this.

to look for trends in timing and type. By mapping out updates month by month and categorizing them (core, spam, reviews, helpful content, and so on), I found that certain months consistently see more activity.

I also noticed some seasonal patterns in the types of updates Google tends to roll out.

While we can’t predict updates with certainty, this analysis (hopefully) gives SEOs a helpful quantitative reference point for planning ahead and managing potential volatility.

Which month is most likely to see an official Google Update?

Based on the full 25-year dataset, the months with the highest number of Google updates are:

• June (7 updates)
• March, May, August (6 updates)
• October, September, February (4 updates each)

Least active months include:

• July, January, and April, each with only 1 recorded update

This broader dataset reinforces late Q2 (May - June) and early Q3 (August) as consistently active periods for updates.

Breaking down updates by month and frequency

The table below doesn’t offer guaranteed timing, but it reflects a pattern-driven forecast based on Google’s update history and the evolving influence of AI systems and user experience priorities.

January

January is typically quiet, with very few recorded updates, making it the least active month historically.

February

February often brings updates to Google’s SEO documentation or systems, with occasional product reviews updates.

March

March is one of the busiest months, frequently featuring core updates and sometimes spam updates released in tandem.

April

April tends to be lighter, but has hosted product review updates and smaller system-level improvements.

May

May is a common time for core updates, often positioned as mid-year algorithm recalibrations.

June

June consistently sees spam updates and, in some years, page experience or link-related changes.

July

July occasionally features a core update or experimental changes, but is generally less predictable.

August

August is a key month for core updates, including some of the most impactful updates in recent years.

September

September is closely associated with helpful content updates and quality refinements focused on user-first content.

October

October is often volatile, with both spam and core updates released close together.

November

November has a strong track record of core updates and updates to review or content systems.

December

December typically wraps up the year with spam or link-focused updates, and sometimes lighter core changes.

What Google's AI pathway could mean for Search updates

AI is already reshaping how Google develops its search systems, and it's likely to influence both the frequency and the format of future updates.

With AI-driven technologies like RankBrain, MUM, and the Search Generative Experience becoming deeply embedded in the ranking framework, I expect updates to become more frequent but less visible.

These updates may not follow the typical algorithm update pattern; instead, they might involve ongoing adjustments to models or quiet retraining efforts that gradually alter how content is interpreted.

The way core updates happen is also likely to evolve. Instead of large-scale updates spaced out over time, we're probably moving toward a model where Google's constantly fine-tuning quality signals using AI insights.

This would mean rankings shift more often, though the changes might feel less abrupt. The boundary between a full update and a test or tweak could also become less clear.

Since AI enables rapid, large-scale experimentation, it’s possible Google will start pushing changes in the background, without the usual heads-up or documentation.

What this points to is a future where the search environment is always in flux.

Rather than relying on traditional update alerts, we might need to lean more on real-time monitoring and a closer watch on performance patterns to understand what’s really happening.