question

Here's a solution that works, but won't perform well on a large dataset due to the 'triangular join' to work out the previous Price.

There some precalculation of the start and end dates - I've presumed you want the full year for any and all of the dates that are present in your original data. From this I can use a 'tally' table (or cte version in this case) to generate all the missing rows, and then a join back to the original data to get the 'last' price to bring forward.

declare @YourTable table (SqNo int, [Month] varchar(10), Price decimal(8,2));

insert into @YourTable select 1,201801,5000;
insert into @YourTable select 2,201804,5250;
insert into @YourTable select 3,201807,5512.5;
insert into @YourTable select 4,201810,5788.12;

set dateformat ymd

declare @startmonth datetime
declare @endmonth datetime

select @startmonth = datefromparts(year(cast(min([Month])+'01' as date)),1,1) from @YourTable;
select @endmonth = datefromparts(year(cast(max([Month])+'01' as date)),12,1) from @YourTable;

with cte_tally as 
(
select top 10000
    ROW_NUMBER() over(order by sc1.column_id) N
from
    sys.columns sc1,sys.columns sc2
)
,cte_month_dates as
(
select
  cast(year(dateadd(month,N-1,@startmonth)) as varchar)
  + right('00'+cast(month(dateadd(month,N-1,@startmonth)) as varchar) ,2) as month_date
from cte_tally
where dateadd(month,N-1,@startmonth) <= @endmonth
)

select
  row_number()over (order by cte_month_dates.month_date) as SqNo, 
  cte_month_dates.month_date as [Month], 
  isnull(YT.Price, PreviousData.Price) as Price
from @YourTable YT
right join cte_month_dates on YT.Month=cte_month_dates.month_date
cross apply (select top 1 price as Price from @YourTable yt2 
              where yt2.Month< cte_month_dates.month_date 
             order by yt2.Month desc) PreviousData
order by cte_month_dates.month_date

returns

SqNo                 Month                            Price
-------------------- -------------------------------- ---------------------------------------
1                    201801                           5000.00
2                    201802                           5000.00
3                    201803                           5000.00
4                    201804                           5250.00
5                    201805                           5250.00
6                    201806                           5250.00
7                    201807                           5512.50
8                    201808                           5512.50
9                    201809                           5512.50
10                   201810                           5788.12
11                   201811                           5788.12
12                   201812                           5788.12


(12 rows affected)